Detecting cognitive biases in interactions with analytics data

ABSTRACT

The present disclosure relates to methods, systems, and non-transitory computer-readable media for determining a cognitive, action-selection bias of a user that influences how the user will select a sequence of digital actions for execution of a task. For example, the disclosed systems can identify, from a digital behavior log of a user, a set of digital action sequences that correspond to a set of sessions for a task previously executed by the user. The disclosed systems can utilize a machine learning model to analyze the set of sessions to generate session weights. The session weights can correspond to an action-selection bias that indicates an extent to which a future session for the task executed by the user is predicted to be influenced by the set of sessions. The disclosed systems can provide a visual indication of the action-selection bias of the user for display on a graphical user interface.

BACKGROUND

In recent years, data analysis systems have improved software platformsfor analyzing digital data and extracting insights from the analysis.For example, many systems provide an analytics user interface thatfacilitates data selection, model implementation, action execution, andreport generation for data analysis. Some systems can further examine aparticular analytical approach (including the model(s) or data used toperform a data analysis) to identify potential biases associated withthe approach that may affect the insights extracted from the data butfail to identify cognitive biases. Accordingly, these systems canfacilitate the removal of biases found in a model or data and improvethe analysis of data accordingly.

Despite these advances, however, conventional data analysis systemssuffer from several technological shortcomings. For example, asmentioned, conventional data analysis systems often provide an analyticsuser interface with which a user (e.g., an analyst) can interact toperform certain analytical tasks. Such conventional systems may allow auser to interact with a user interface to select various digital actionsthat, when executed in combination, result in completion of a particulartask. Some such systems, however, are often configured in a way thatencourages the user to select the same sequence of digital actions for aparticular task, resulting in a narrow and biased approach to dataanalysis. In contrast to biases influenced by a system's configuration,human behavior (e.g., rooted in a psychological basis) can lead users toselect digital actions in accordance with personally-held cognitivebiases. Conventional systems typically fail to deter or at leastrecognize adherence to these cognitive biases.

Additionally, conventional data analysis systems can also operateinflexibly. For example, though conventional data analysis systems canexamine analytical approaches to identify potential biases, such systemsare often limited to the detection of biases that are inherent to thedata to be analyzed or the model(s) used for the analysis. Indeed, thesesystems often fail to detect analytical defects that extend beyond thesetypes of biases, such as by failing to detect defects caused bycognitive biases associated with the user performing the analysis. Suchsystems risk performing a data analysis that suffers from theseundetected biases. While there have been previous efforts to exploreother types of biases, outside of cognitive biases, that may affect anapproach to data analysis, these efforts fail to address human cognitivebias that can affect how a user performs data analysis. Further, theseefforts have largely been limited to human experimental studies thatposit various scenarios to a subject and elicit responses accordingly.Such an approach, however, is not scalable.

SUMMARY

This disclosure describes one or more embodiments of methods,non-transitory computer-readable media, and systems that solve one ormore of the foregoing problems and provide other benefits. For example,in one or more embodiments, the disclosed systems analyze, utilizing amachine learning model, previously-observed digital action sequencesselected by a user when executing a particular task to generate weightsthat indicate an action-selection bias of the user. Indeed, thedisclosed systems can utilize the weights to indicate a cognitivebias—such as an anchoring bias or a recency bias—that influences how theuser will select a sequence of digital actions for future execution ofthe task. In one or more embodiments, the disclosed systems generate avisual indication that can include, for instance, a visualrepresentation of the weights (e.g., in graph-form) or a prompt (e.g.,provided via an intelligent agent) encouraging the user to select adifferent sequence of digital actions. Thus, the disclosed systemsintroduce an unconventional approach that utilizes machine learning toflexibly identify biases that affect a user's action-selection processand the results derived from that process. In some cases, the disclosedsystems can identify such biases to facilitate improving a user's workto correct for cognitive biases.

Additional features and advantages of one or more embodiments of thepresent disclosure are outlined in the following description.

BRIEF DESCRIPTION OF THE DRAWINGS

This disclosure will describe one or more embodiments of the inventionwith additional specificity and detail by referencing the accompanyingfigures. The following paragraphs briefly describe those figures.

FIG. 1 illustrates an example system environment in which a biasdetection system can operate in accordance with one or more embodiments.

FIG. 2 illustrates an overview diagram of the bias detection systemgenerating a visual indication of an action-selection bias of a user inaccordance with one or more embodiments.

FIG. 3 illustrates an overview of the bias detection system identifyinga set of digital action sequences from a digital behavior log inaccordance with one or more embodiments.

FIGS. 4A-4B illustrate diagrams of an attention neural network used togenerate attention weights in accordance with one or more embodiments.

FIGS. 5A-5B illustrate block diagrams for a multi-phase process forgenerating attention weights that correspond to a user in accordancewith one or more embodiments.

FIG. 6A illustrates a graphical user interface used by the biasdetection system to display graphical representations of sets of sessionweights corresponding to a set of users in accordance with one or moreembodiments.

FIG. 6B illustrates a graphical user interface used by the biasdetection system to display a graphical representation of sessionweights corresponding to a user in accordance with one or moreembodiments.

FIG. 6C illustrates a graphical user interface used by the biasdetection system to display a graphical representation of frequencies ofdigital actions selected by the user in accordance with one or moreembodiments.

FIG. 6D illustrates a graphical user interface used by the biasdetection system to display a frequency heat map indicating frequenciesof digital actions selected by the user in accordance with one or moreembodiments.

FIG. 7 illustrates a graphical user interface used by the bias detectionsystem to display a visual indication of an action-selection bias of auser based on the user selecting digital actions consistent with theaction-selection bias in accordance with one or more embodiments.

FIG. 8 illustrates an example schematic diagram of a bias detectionsystem in accordance with one or more embodiments.

FIG. 9 illustrates a flowchart of a series of acts for generating avisual indication of an action-selection bias of a user in accordancewith one or more embodiments.

FIG. 10 illustrates a block diagram of an exemplary computing device inaccordance with one or more embodiments.

DETAILED DESCRIPTION

The disclosure describes one or more embodiments of a bias detectionsystem utilizing machine learning to identify how a user depends onprevious executions of a task when selecting digital actions for asubsequent execution of the task. For example, the bias detection systemcan utilize a machine learning model to analyze sequences of digitalactions previously selected by a user to execute a particular task. Thebias detection system can utilize the machine learning model to generatevarious weights assigned to the previous executions of the task based onthe analysis. These weights can represent, for example, a cognitive biasthat indicates that the user relies on early executions of the task(e.g., anchoring bias) or more recent executions of the task (e.g.,recency bias) when selecting a sequence of digital actions for asubsequent execution of the task. The disclosed systems can inform theuser (e.g., via a graph or notification displayed within a graphicaluser interface) of the action-selection bias indicated by the weights.

To provide an illustration, in one or more embodiments, the biasdetection system identifies, from a digital behavior log correspondingto a user, a set of digital action sequences corresponding to a set ofsessions for a task executed by the user. The bias detection system cangenerate, utilizing a machine learning model, session weights indicatingan extent a future session for the task is predicted to be influenced bythe set of sessions. Based on the session weights, the bias detectionsystem can provide a visual indication of an action-selection bias ofthe user for the task for display on a graphical user interface.

As just mentioned, in one or more embodiments, the bias detection systemidentifies a set of digital action sequences that correspond to a set ofsessions for a task (e.g., the same task) from a digital behavior logassociated with a user. Indeed, the bias detection system can maintain adigital behavior log for a user that stores the digital actions selectedby the user (e.g., via a user interface) when executing tasks. In someinstances, the bias detection system identifies a digital actionsequence corresponding to a session for a task by identifying, withinthe digital behavior log, a task-identifying digital action (e.g., adigital action that is unique to the particular task) and building acontext by selecting digital actions that precede and follow thetask-identifying digital action.

Further, as mentioned, in one or more embodiments, the bias detectionsystem generates session weights utilizing a machine learning model. Inparticular, the bias detection system can utilize the machine learningmodel to generate the session weights based on the set of sessions(e.g., the corresponding set of digital action sequences) identifiedfrom the digital behavior log of the user. In some instances, themachine learning model analyzes the set of sessions using rollingwindows of sessions where each window includes a session thatimmediately follows the sessions of the preceding window. Morespecifically, in some cases, each subsequent window can include thefollowing session and drop the first session of the previous window.

In one or more embodiments, the machine learning model includes a neuralnetwork, such as an attention neural network. For example, the machinelearning model can include a hierarchical attention neural network thatperforms an action-level analysis and a task-level analysis on the setof sessions (e.g., the corresponding set of digital action sequences)identified from the digital behavior log. Accordingly, in someembodiments, the session weights include the attention weights generatedby the attention neural network.

The session weights can indicate a predicted degree to which the userwill rely on the set of sessions for future execution of the task. Forexample, the session weights can indicate that future execution of thetask is predicted to be based on a particular action-selection bias—suchas an anchoring bias or a recency bias—of the user. In other words, insome embodiments, the bias detection system determines that, when a userselects a digital action sequence for execution of a task, the userbases the selection on previous digital action sequences selected by theuser for previous sessions for the task—or, at least, selects digitalactions consistent with one or more of those previous sessions for thetask. Thus, in some embodiments, the bias detection system can utilizethe session weights to generally indicate whether the user is biased infavor of earlier sessions for the task or later sessions for the taskpreviously executed by the user when selecting a digital action sequencefor subsequent execution of the task.

In some embodiments, the bias detection system accounts for commondigital action sequences or sub-sequences selected for execution of atask when generating the session weights. Indeed, some tasks may requireselection of certain digital actions (e.g., sequences or sub-sequencesof digital actions), or standard practice may dictate selection ofcertain digital actions. Accordingly, the bias detection system candetect and account for such commonly selected digital actions (as notindicating cognitive bias) when generating or updating session weightstailored to a particular user.

Additionally, as mentioned above, in one or more embodiments, the biasdetection system provides a visual indication of the action-selectionbias of the user. For example, the bias detection system can generate agraphical representation of the session weights indicating theaction-selection bias (e.g., a graph of the values of the sessionweights). As another example, the bias detection system can generate anotification for display (e.g., via a virtual assistant or otherintelligent agent) to the user in response to detecting that the user isselecting one or more digital actions for a task in a sequenceconsistent with the action-selection bias of the user. Thus, the biasdetection system can inform the user of certain biases that affect thedigital action sequences selected by the user to execute a task.

The bias detection system provides several advantages over conventionalsystems. For example, the bias detection system introduces anunconventional approach for identifying biases that affect how usersselect sequences of digital actions when executing tasks. Indeed, thebias detection system utilizes an unconventional ordered combination ofactions for determining an action selection-bias of a user based onsession weights generated by a machine learning model analyzingpreviously-observed data of the user (e.g., from a digital behavior logof the user) and providing a visual indication of that action-selectionbias. In other words, the bias detection system introduces a process foridentifying an action-selection bias of a user that is not utilized byconventional systems. Thus, the bias detection system can encourageusers to select a different sequence of digital actions when executing atask to obtain different results and/or new insights into the data.Further, by utilizing a machine learning model to generate the sessionweights, the bias detection system generates bias-indicative values thatcould not be determined by humans. For example, the bias detectionsystem can generate feature vectors (e.g., action-level context vectorsor session-level context vectors) that include values that representlatent features corresponding to digital action sequences selected by auser.

Further, by providing indications of biases that affect selection ofdigital actions used in executing a task, the bias detection system canoperate more flexibly than conventional systems. Indeed, the biasdetection system can identify and provide visual indications for biasesthat extend beyond those that are inherent to the digital data analyzedor the model(s) used to perform the analysis. By utilizing a machinelearning model to generate the sessions weights upon which the visualindication of the action-selection bias is based, the bias detectionsystem further provides improved scalability with regard to theidentification of such biases compared to previous efforts. Indeed, thebias detection system offers a practical solution for determining theaction-selection biases of a large number of users—while detecting theaction-selection biases for each user individually—by analyzing digitalaction sequences previously selected by those users. Further, byanalyzing previously-selected digital action sequences, the biasdetection system can operate passively in many instances (e.g., withoutengaging the user), allowing for an analytical approach that isindependent of user availability.

As illustrated by the foregoing discussion, the present disclosureutilizes a variety of terms to describe features and benefits of thebias detection system. As used herein, the term “digital action” refersto an action executed using a computing device. In particular, a digitalaction can refer to an action performed by a user of a computing device,using functions and features of the computing device. For example, adigital action can include an action related to analyzing digital data(e.g., via an analytics user interface), such as launching a project,dragging-and-dropping one or more components, building a segment, savinga segment, clicking a node, calculating a value, generating a report, orinteracting with a report. However, a digital action can include anaction other than those in the context of digital data analysis. Forexample, a digital action can include an action related to generating,viewing, or editing a digital image or digital video or a digital actionrelated to conducting a search or navigating a website.

Relatedly, as used herein, the term “digital action sequence” refers toa group of sequentially-ordered digital actions. In particular, adigital action sequence can refer to a plurality of digital actions thatwere selected by a user and are ordered based on the order in whichthose digital actions were selected or executed. In one or moreembodiments, a digital action sequence corresponds to a particular task.For example, a digital action sequence can include a set of all digitalactions or a subset of digital actions selected or executed to completea task. In some embodiments, however, a sequence of digital actionscorresponds to multiple tasks. As used herein, the term “predicteddigital action sequence” refers to a digital action sequence predictedto be selected by a user for completion of a task. As used herein, theterm “observed digital action sequence” refers to a previous (e.g.,recorded) digital action sequence selected by a user for completion of atask. In one or more embodiments, the bias detection system can utilizean observed digital action sequence of as a ground truth for comparisonwith a predicted digital action sequence in adjusting the sessionweights of a machine learning model.

Additionally, as used herein, the term “digital behavior log” refers toa digital log of digital actions executed by a user. In particular, adigital behavior log can refer to a digital record that stores digitalactions executed by a client device associated with the user (e.g., inresponse to input from the user and/or under a user profile associatedwith the user). A digital behavior log can include a digital record thatstores a chronological list of digital actions or otherwise includes atimed record of digital actions selected by a user via one or moreplatforms, operating systems, computer applications, and/or userinterfaces.

Further, as used herein, the term “task” refers to an objective that iscompleted via performance of a plurality of digital actions by acomputing device based on user input. In particular, a task can refer toa function or operation that is completed (e.g., executed) or performedby a computing device as a result of performance of a plurality ofdigital actions or that becomes available for completion afterperformance of the plurality of digital actions. In other words, a taskcan refer to an objective and can be composed of one or more digitalactions selected and/or performed to complete (e.g., execute) thatobjective. For example, a task can include performance of an analysis ofdigital data, such as building a segment of users, a segmentationanalysis, or a contribution analysis. As another example, a task caninclude generating, viewing, or editing a digital image or digital videoor at least a portion of a digital image or a digital video. A task canalso include conducting a search or navigating a website.

Additionally, as used herein, the term “task-identifying digital action”refers to a digital action that can be used to detect or otherwiseidentify a task. For example, a task-identifying digital action caninclude a digital action that is unique to a particular task or isotherwise designated as useful for identifying (or indicative of) aparticular task.

As used herein, the term “session” refers to an instance of executing atask and a corresponding digital action sequence. In particular, asession can refer to a distinct occasion in which a user selects one ormore digital actions for execution to complete a task. For example, asession can refer to an instance of executing a task that isdistinguishable (e.g., distinct in time) from another instance ofexecuting the same task. As used herein, the term “set of sessions”refers to a collection of multiple sessions. For example, a set ofsessions can refer to a chronological grouping of sessions for a task(e.g., the same task) where a given session chronologically precedes theimmediately following session in the set and/or chronologically followsthe immediately preceding session in the set. In some embodiments, thebias detection system determines the chronological order for sessionsfor a task based on the order in which those sessions appear within thedigital behavior log of the corresponding user (or based on a timestampassociated with each of the sessions within the digital behavior log).

Additionally, as used herein, the term “rolling windows of sessions”refers to overlapping sets of sessions for a task. In particular,rolling windows of sessions can refer to a collection of various sets ofsessions for a task where each “window” corresponds to a set ofsessions, and the rolling windows of sessions, as a whole, progressesthrough the various sets of sessions in the collection. Each window canoverlap with at least one other window by including one or more sessionsincluded in the at least one other window. For example, a given windowcan include all but one session included in the adjacent (e.g.,preceding or subsequent) window. In one or more embodiments, the biasdetection system establishes rolling sessions of windows based on achronological order. To illustrate, the bias detection system can ordera collection of sessions for a task (e.g., and corresponding to the sameuser) chronologically. Thus, the windows roll (i.e., progress) throughthe collection of sets of sessions chronologically.

Further, as used herein, the term “action-selection bias” refers to abias associated with a user that indicates the extent to which actionspreviously selected by the user influence a future selection of actionsby the user. In particular, an action-selection bias can refer to acognitive bias that indicates the extent to which digital actionsselected during previous sessions for a task executed by a userinfluence the selection of digital actions by the user in a futuresession of the same task. An action-selection bias can more particularlyrefer to an action-selection bias that indicates the extent to whichdigital action sequences associated with previous sessions for a taskexecuted by a user influence a digital action sequence selected by theuser in a future session of the same task. In one or more embodiments,an action-selection bias recognizes and accounts for selections ofdigital actions common or standard to a task (e.g., required by the taskor standard practice for the task) and detects a cognitive biasindicated by selected actions beyond such common or standard actionsubsequences. An action-selection bias can include, for example, acognitive bias, such as an anchoring bias or a recency bias. But anaction-selection bias does not refer to a bias created by a modelspecification or the selections made by a human in designing a model. Asused herein, the term “anchoring bias” refers to a bias in which early(e.g., initial) sessions for a task executed by a user have a relativelygreater influence on future executions of the task by the user thanlater (e.g. more recent) sessions. By comparison, as used herein, theterm “recency bias” refers to a bias in which later sessions for a taskexecuted by a user have a relatively greater influence on futureexecutions of the task by the user.

As used herein, a “machine learning model” refers to a computerrepresentation that can be tuned (e.g., trained) based on inputs toapproximate unknown functions. In particular, the term “machine-learningmodel” can include a model that utilizes algorithms to learn from, andmake predictions on, known data by analyzing the known data to learn togenerate outputs that reflect patterns and attributes of the known data.For instance, a machine-learning model can include but is not limited toa neural network (e.g., a convolutional neural network, recurrent neuralnetwork or other deep learning network), a decision tree (e.g., agradient boosted decision tree), association rule learning, inductivelogic programming, support vector learning, Bayesian network,regression-based model (e.g., censored regression), principal componentanalysis, or a combination thereof.

As mentioned, a machine learning model can include a neural network. Asused herein, the term “neural network” refers to a machine learningmodel that includes a model of interconnected artificial neurons(organized in layers) that communicate and learn to approximate complexfunctions and generate outputs based on a plurality of inputs providedto the model. In addition, a neural network can comprise an algorithm(or set of algorithms) that implements deep learning techniques thatutilize a set of algorithms to model high-level abstractions in data.

A neural network can include an attention neural network. As usedherein, the term the term “attention neural network” can refer to aneural network having one or more attention features. In particular, anattention neural network can refer to a neural network having one ormore attention mechanisms (e.g., attention layers). In some embodiments,an attention neural network includes a hierarchical attention model. Asused herein, the term “attention mechanism” refers to a neural networkcomponent that generates values corresponding to attention-controlledfeatures. In particular, an attention mechanism can generate valuesbased on one or more hidden states (e.g., an output state and/or a finalstate). For example, an attention mechanism can be trained to controlaccess to memory, allowing certain features to be stored and lateraccess while processing neural network inputs to learn the context of agiven input (i.e., a given hidden state corresponding to the input)without relying solely on that input. In one or more embodiments, anattention mechanism corresponds to a particular neural network layer andprocesses the outputs (e.g., the output states) generated by the neuralnetwork layer.

As used herein, the term “action-level encoder” refers to a neuralnetwork encoder that performs an action-level analysis on arepresentation of a digital action sequence. In particular, anaction-level encoder can refer to a neural network encoder that analyzesencoded representations of digital actions individually. As used herein,the term “action-level context vector” refers to a vector of values(e.g., latent or hidden values) generated by an action-level encoder(e.g., based on an analysis of the digital actions of a digital actionsequence).

Further, as used herein, the term “session-level encoder” refers to aneural network encoder that performs a session-level analysis on arepresentation of a digital action sequence. In particular, asession-level encoder can refer to a neural network encoder thatanalyzes an encoded representation of a digital action sequence as awhole. As used herein, the term “session-level context vector” refers toa vector of values (e.g., latent or hidden values) generated by asession-level encoder (e.g., based on an analysis of a digital actionsequence of as a whole).

As further used herein, the term “session weight” refers to a value thatcorresponds to one or more sessions for a task. In particular, a sessionweight can refer to a value that is generated using a machine learningmodel and indicates the extent to which one or more correspondingsessions of a task influence a future session for the task. For example,a session weight can include a machine learning model weight (e.g., aweight internal to the machine learning model) that is generated (e.g.,learned) by analyzing digital action sequences for a task previouslyselected by one or more users. As indicated above, a session weight canindicate an extent a future session for a task is predicted to beinfluenced by a set of sessions—beyond an influence from an actionsequence from past sessions required or standardized by a task or by ananalysis guided by a user interface. A session weight can accordinglyexclude influence from selections of digital actions that are common orstandard to a task.

Additionally, as mentioned above, a machine learning model can include aneural network, such as an attention neural network. Accordingly, in oneor more embodiments, a session weight can include an attention weight.As used herein, the term “attention weight” refers to a session weightgenerated using an attention neural network. In particular, an attentionweight can refer to a session weight generated using a neural attentionmechanism of an attention neural network. Relatedly, as used herein, theterm “baseline attention weight” more particularly refers to anattention weight that is generated (e.g., learned) by analyzing digitalaction sequences for a task previously selected by a plurality of users.Indeed, a baseline attention weight can refer to a value generated by anattention mechanism that represents one or more characteristics orfeatures associated with a task that lead to the commonalities in thedigital action sequences selected by the plurality of users. Thus, thebias detection system can compare an attention weight that correspondsto a particular user with a corresponding baseline attention weight todetermine how the user deviates from a baseline.

As used herein, the term “visual indication” refers to a visual elementthat can be displayed on a computing device. In particular, a visualindication refers to a visual component that can be displayed on aclient device in association with underlying data, such as sessionweights generated by a machine learning model. For example, a visualindication can include a graphical indication (e.g., a visual indicationhaving a graphics element) or, more particular, a graphicalrepresentation (e.g., a graph, a chart, a table, a diagram, a heat map)of the underlying data. In some embodiments, a visual indication isinteractable. In other words, in response to receiving an interactionwith the visual indication, the bias detection system can provideadditional information (e.g., via one or more additional visualindications) or perform some other operation.

Additionally, as used herein, the term “frequency of a digital action”refers to selections of a digital action during a session for a task.For example, a frequency of a digital action can refer to a total numberof selections of a digital action during a session for a task or afraction of selections out of all selections made during the session forthe task that were directed to the digital action. As used herein, theterm “frequency heat map” refers to a heat map that displays thefrequencies of digital actions across one or more sessions for a task.

Additional detail regarding the bias detection system will now beprovided with reference to the figures. For example, FIG. 1 illustratesa schematic diagram of an exemplary system 100 in which a bias detectionsystem 106 can be implemented. As illustrated in FIG. 1 , the system 100can include a server(s) 102, a network 108, an administrator device 110,client devices 114 a-114 n, an analytics database 118, and a third-partyserver 120.

Although the system 100 of FIG. 1 is depicted as having a particularnumber of components, the system 100 can have any number of additionalor alternative components (e.g., any number of servers, administratordevices, client devices, analytics databases, third-party servers, orother components in communication with the bias detection system 106 viathe network 108). Similarly, although FIG. 1 illustrates a particulararrangement of the server(s) 102, the network 108, the administratordevice 110, the client devices 114 a-114 n, the analytics database 118,and the third-party server 120, various additional arrangements arepossible.

The server(s) 102, the network, 108, the administrator device 110, theclient devices 114 a-114 n, the analytics database 118, and thethird-party server 120 may be communicatively coupled with each othereither directly or indirectly (e.g., through the network 108 discussedin greater detail below in relation to FIG. 10 ). Moreover, theserver(s) 102, the administrator device 110, the client devices 114a-114 n, and the third-party server 120 may include a variety ofcomputing devices (including one or more computing devices as discussedin greater detail with relation to FIG. 10 ).

As mentioned above, the system 100 includes the server(s) 102. Thesystem 100 can generate, store, receive, and/or transmit digital data,including digital data related to digital actions and action-selectionbiases. For example, the server(s) 102 can receive (e.g., from thethird-party server 120) a digital behavior log that includes digitalactions selected by a user to execute one or more tasks and provide avisual indication of an action-selection bias of the user for display ona client device (e.g., one of the client devices 114 a-114 n). In one ormore embodiments, the server(s) 102 comprise a data server. Theserver(s) 102 can also comprise a communication server or a web-hostingserver.

As shown in FIG. 1 , the server(s) 102 can include an analytics system104. In particular, the analytics system 104 can collect, manage, and/orutilize analytics data. For example, the analytics system 104 cancollect analytics data related to digital actions selected by users toexecute a task. The analytics system 104 can collect the analytics datain a variety of ways. For example, in one or more embodiments, theanalytics system 104 causes the server(s) 102 to track digital actionsselected by users via the client devices 114 a-114 n and report thedigital actions for storage (e.g., in the form of a digital behaviorlog) on a database (e.g., the analytics database 118). In someembodiments, the third-party server 120 tracks the digital actions andstores them within the analytics database 118; accordingly, theanalytics system 104 can retrieve the digital actions tracked by thethird-party server 120 from the analytics database 118.

In some embodiments, the analytics system 104 receives the analyticsdata directly from the client devices 114 a-114 n. For example, theanalytics system 104 can provide a user interface through which theclient devices 114 a-114 n can select digital actions (e.g., ananalytics user interface through which the client devices 114 a-114 ncan select digital actions to perform data analysis). The analyticssystem 104 can receive or otherwise detect the digital action selectionsmade by the client devices 114 a-114 n and store the selected digitalactions in the analytics database 118. It should be noted, however, thatthe bias detection system 106 is not limited to the context of digitalactions selected to perform data analysis. Indeed, the bias detectionsystem 106 can similarly collect, manage, and/or utilize digital actionsselected for a variety of purposes.

Additionally, the server(s) 102 include the bias detection system 106.In particular, in one or more embodiments, the bias detection system 106utilizes the server(s) 102 to analyze digital action sequencescorresponding to tasks executed by users and generate visual indicationsof task-selection biases of those users. For example, the bias detectionsystem 106 can utilize the server(s) 102 to identify digital actionsequences corresponding to a task executed by a user and, based on thosedigital action sequences, generate a visual indicator of anaction-selection bias of the user.

To illustrate, in one or more embodiments, the bias detection system106, via the server(s) 102, identifies, from a digital behavior logassociated with a user, a set of digital action sequences correspondingto a set of sessions for a task executed by the user. Via the server(s)102, the bias detection system 106 can utilize a machine learning modelto generate sessions weights that indicate an extent to which a futuresession for the task is predicted to be influenced by the set ofsessions. The bias detection system 106, via the server(s) 102, canfurther provide a visual indication of an action-selection bias of theuser for the task for display on a graphical user interface based on thegenerated session weights.

In one or more embodiments, the analytics database 118 stores digitaldata related to digital actions selected by users for execution of oneor more tasks. For example, the analytics database 118 can store digitalbehavior logs corresponding to users, where the digital behavior log ofa particular user includes digital data related to digital actionsselected by the user for execution of one or more tasks. Though FIG. 1illustrates the analytics database 118 as a distinct component, one ormore embodiments include the analytics database 118 as a component ofthe server(s) 102, the analytics system 104, or the bias detectionsystem 106.

In one or more embodiments, the third-party server 120 tracks, detects,or otherwise identifies digital actions selected by users, via clientdevices, for the execution of one or more tasks. For example, in one ormore embodiments, the third-party server 120 can be accessed by a clientdevice (e.g., one of the client devices 114 a-114 n) to select digitalactions for the execution of one or more tasks. Indeed, like theanalytics system 104, the third-party server 120 can provide a userinterface through which the client devices 114 a-114 n can selectdigital actions (e.g., an analytics user interface through which theclient devices 114 a-114 n can select digital actions to perform dataanalysis).

In one or more embodiments, the administrator device 110 includes acomputing device that can access and display digital data related to theaction-selection biases of users. For example, the administrator device110 can include a smartphone, a tablet, a desktop computer, a laptopcomputer, a head-mounted display device, or another electronic device.The administrator device 110 can include one or more applications (e.g.,the analytics application 112) that can access and display digital datarelated to the action-selection biases of users. For example, theanalytics application 112 can include a software application installedon the administrator device 110. Additionally, or alternatively, theanalytics application 112 can include a software application hosted onthe server(s) 102, which may be accessed by the administrator device 110through another application, such as a web browser.

In one or more embodiments, the client devices 114 a-114 n includecomputing devices that can select digital actions for execution of oneor more tasks. For example, the client devices 114 a-114 n can includesmartphones, tablets, desktop computers, laptop computers,head-mounted-display devices, or other electronic devices. The clientdevices 114 a-114 n can include one or more applications (e.g., clientapplications 116 a-116 n, respectively) that can select digital actionsfor execution of one or more tasks. For example, the client applications116 a-116 n can each include a software application respectivelyinstalled on the client devices 114 a-114 n. Additionally, oralternatively, the client applications 116 a-116 n can each include aweb browser or other application that accesses a software applicationhosted on the server(s) 102.

The bias detection system 106 can be implemented in whole, or in part,by the individual elements of the system 100. Indeed, although FIG. 1illustrates the bias detection system 106 implemented with regard to theserver(s) 102, different components of the bias detection system 106 canbe implemented by a variety of devices within the system 100. Forexample, one or more (or all) components of the bias detection system106 can be implemented by a different computing device (e.g., one of theclient devices 114 a-114 n) or a separate server from the server(s) 102hosting the analytics system 104 (e.g., the third-party server 120).Example components of the bias detection system 106 will be describedbelow with regard to FIG. 8 .

As mentioned above, the bias detection system 106 can generate a visualindication of an action-selection bias of a user. FIG. 2 illustrates anoverview diagram of the bias detection system 106 generating a visualindication of an action-selection bias of a user in accordance with oneor more embodiments.

As shown in FIG. 2 , the bias detection system 106 identifies a digitalbehavior log 202 corresponding to a user. In one or more embodiments,the bias detection system 106 identifies the digital behavior log 202 byreceiving the digital behavior log 202 from a computing device (e.g., aclient device, an administrator device, or a third-party server). Insome embodiments, the bias detection system 106 identifies the digitalbehavior log 202 by accessing a database storing digital behavior logs.For example, the bias detection system 106 can maintain a database andstore digital behavior logs corresponding to users therein. In someinstances, an external device or system stores digital behavior logs foraccess by the bias detection system 106.

As further shown in FIG. 2 , the bias detection system 106 utilizes amachine learning model 204 to analyze the digital behavior log 202. Inparticular, as discussed below, the bias detection system 106 utilizesthe machine learning model 204 to analyze a set of digital actionsequences identified from the digital behavior log 202. The set ofdigital action sequences can correspond to a set of sessions for a taskexecuted by the user.

In one or more embodiments, the machine learning model includes a neuralnetwork, such as an attention neural network. The architecture of anattention neural network utilized by the bias detection system 106 toanalyze the digital behavior log 202 in one or more embodiments will bediscussed below with reference to FIGS. 4A-4B. It should be noted,however, that the bias detection system 106 can utilize various othermachine learning models (e.g., linear or logistic regression models,decision trees), including various other neural network architectures(e.g., various recurrent neural network architectures), to analyze thedigital behavior log 202 (e.g., the digital action sequences from thedigital behavior log 202) as needed.

Additionally, as shown in FIG. 2 , the bias detection system 106utilizes the machine learning model 204 to generate sessions weights 206based on the analysis of the digital behavior log 202 (e.g., based onthe analysis of the set of digital action sequences that correspond tothe set of sessions for the task). In one or more embodiments, thesession weights indicate an extent to which a future session for thetask is predicted to be influenced by the set of sessions that werepreviously executed by the user. Indeed, the sessions weights canindicate an action-selection bias associated with a user's reliance onor favoritism toward previously-selected digital action sequences whenselecting a sequence of digital actions for execution of the task. Inone or more embodiments, the bias detection system 106 further extractsthe session weights 206 from the machine learning model 204.

As described below, in one or more embodiments, the bias detectionsystem 106 utilizes the machine learning model 204 to generate thesession weights 206 based on an analysis of sets of digital actionsequences corresponding to sets for sessions for the task executed bythe user. For example, the bias detection system 106 can utilize themachine learning model 204 to analyze digital action sequences thatcorrespond to all sessions (or a large number of sessions) for the taskexecuted by the user rather than a set of sessions that covers only aportion of those sessions, which may include only relatively few of thesessions for the task executed by the user. Thus, in one or moreembodiments, the session weights 206 do not correspond to particularsessions from the sets of sessions. Rather, the session weights 206correspond to a generalized window that indicates, based on the valuesof the session weights, whether the corresponding user favors earliersessions previously executed by the user or later sessions previouslyexecuted by the user.

In one or more embodiments, the bias detection system 106 furthergenerates a visual indication of the action-selection bias of the userbased on the session weights 206. For example, as shown in FIG. 2 , thebias detection system 106 can generate a visual indication similar toone of the visual indications 208 a-208 c based on the values of thesession weights 206. Though FIG. 2 illustrates the visual indications208 a-208 c as graphs, the bias detection system 106 can generatevarious other forms of visual indications (e.g., frequency heat maps,notifications).

In one or more embodiments, an action-selection bias of a user includesa recency bias of the user. Accordingly, the bias detection systemgenerates a visual indicator that represents this recency bias (e.g.,the visual indicator 208 a). In some embodiments, the action-selectionbias of the user includes an anchoring bias. Accordingly, the biasdetection system 106 generates a visual indicator that represents theanchoring bias (e.g., the visual indicator 208 c). In some instances,however, the bias detection system 106 determines that the user is notassociated with an action-selection bias and generates a visualindication (e.g., the visual indicator 208 b) accordingly.

As mentioned above, the bias detection system 106 utilizes a machinelearning model to analyze a set of digital action sequences that havebeen identified from a digital behavior log of the user. Indeed, the setof digital action sequences can correspond to a set of sessions for atask executed by the user. FIG. 3 illustrates an overview of a sequenceof steps that the bias detection system 106 performs for identifying aset of digital action sequences from a digital behavior log inaccordance with one or more embodiments. Though FIG. 3 illustrates thebias detection system 106 performing the steps in a particular sequence,the bias detection system 106 can perform the steps in differentsequence orders as well.

For instance, as shown in FIG. 3 , the bias detection system 106performs an act 302 of encoding the digital actions of a digitalbehavior log. In particular, the bias detection system 106 can encodethe digital actions of the digital behavior log within a categoricalaction space. For example, the number of digital actions available forselection can be very high. Accordingly, the bias detection system 106can establish a categorical action space that includes a number ofaction categories. The bias detection system 106 can determine thenumber of action categories based on a pre-determined number of actioncategories or based on input received from a user (e.g., input receivedfrom an administrator via an administrator device).

The bias detection system 106 can further encode the digital actions ofthe digital behavior log by assigning each digital action to one of theaction categories within the categorical actions space. In one or more,the bias detection system 106 assigns a plurality of digital actions tothe same action category (e.g., where those digital actions arerelated). In some instances, however, the bias detection system 106 canassign only one digital action to an action category (e.g., the actioncategory includes a single digital action). In some embodiments, thebias detection system 106 encodes the digital actions of the digitalbehavior log using one-hot encoding. Thus, the bias detection system 106can reduce the dimensionality and noise of the data to be analyzed bythe machine learning model by grouping together related digital actions.In one or more embodiments, the bias detection system 106 generates anencoded digital behavior log for the user that includes the encodings ofthe digital actions from the digital behavior log corresponding to theuser.

As shown in FIG. 3 , the bias detection system 106 further performs anact 304 of identifying a digital action sequence that includes atask-identifying digital action to represent a task. For example, thebias detection system 106 can identify, from the encoded digitalbehavior log corresponding to the user, a task-identifying digitalaction (e.g., an encoded task-identifying digital action, such as “Group3 (Segment Builder Load)”) that corresponds to a desired task.

The bias detection system 106 can further select, from the encodeddigital behavior log, a set of digital actions (e.g., encoded digitalactions) from within a threshold number of digital actions of thetask-identifying digital action. To illustrate, the bias detectionsystem 106 can select a first subset of digital actions thatchronologically precedes the task-identifying digital action and iswithin a threshold number of digital actions from the task-identifyingdigital action (e.g., the encoded digital actions “Group 1 (LaunchingProject) . . . Group 2 (Dragdrop Operation)”). Additionally, the biasdetection system 106 can select a second subset of digital actions thatchronologically follows the task-identifying digital action and iswithin the threshold number of digital actions from the task-identifyingdigital action (e.g., the encoded digital actions “Group 8 (SegmentationCreation) . . . Group 2 (Dragdrop Operation)”). The bias detectionsystem 106 can determine the threshold number of digital actions basedon a pre-determined threshold number of digital actions or based oninput received from a user (e.g., input received from an administratorvia an administrator device). In some embodiments, the bias detectionsystem 106 establishes the threshold number of digital actions based onan average number of digital actions selected per session of a task or afrequency distribution indicating the numbers of digital actionsselected per session for a task.

In one or more embodiments, the bias detection system 106 determinesthat the number of digital actions that chronologically follows and/orchronologically precedes the task-identifying digital action is lessthan the threshold number of digital actions. In response, the biasdetection system 106 can pad the remainder of the digital actionsequence with zeroes or null values. In some embodiments, the biasdetection system 106 determines that another task-identifying digitalaction that corresponds to the task is within the threshold number ofdigital actions. In response, the bias detection system 106 can reducethe threshold number of digital actions selected around the (initial)task-identifying digital action. Thus, the bias detection system 106selects or otherwise identifies a digital action sequence thatcorresponds to a session for a task executed by the user.

As mentioned above, the bias detection system 106 can perform the stepsillustrated by FIG. 3 in different sequence orders. Accordingly, thebias detection system 106 can identify the digital action sequence andthen encode the digital actions of the digital action sequence in someembodiments.

The bias detection system 106 can repeat the act 304 to identify a setof digital action sequences that correspond to a set of sessions for thetask. For example, in one or more embodiments, the bias detection system106 identifies all digital action sequences that correspond to a sessionfor the task. In some embodiments, the bias detection system 106identifies a pre-established number of digital action sequences thatcorrespond to a session for the task.

As mentioned above, the bias detection system 106 can utilize a machinelearning model to generate session weights based on a set of digitalaction sequences identified from the digital behavior log of a user andcorrespond to a set of sessions for a task executed by the user. Asfurther mentioned, in one or more embodiments, the machine learningmodel includes a neural network, such as an attention neural network.Accordingly, the session weights can include attention weights generatedutilizing the attention neural network. In accordance with one or moreembodiments, FIGS. 4A-4B illustrate diagrams of an attention neuralnetwork that can generate attention weights based on a set of digitalaction sequences corresponding to a user. In particular, FIG. 4Aillustrates a block diagram providing a broad overview of thearchitecture of an attention neural network as well as the inputs andoutputs of the attention neural network. FIG. 4B illustrates a diagramthat provides more detail regarding the values generated by the variouscomponents of the attention neural network.

As shown in FIG. 4A, the bias detection system 106 provides a set ofdigital action sequences 404 to the attention neural network 402. Theset of digital action sequences 404 can include a plurality of digitalaction sequences identified from a digital behavior log corresponding toa user, as discussed above with reference to FIG. 3 . Further, the setof digital action sequences 404 can include digital action sequencesthat correspond sessions for the same task.

In one or more embodiments, the set of digital action sequences 404 doesnot include all digital action sequences from the digital behavior logof the user that correspond to sessions for the same task. Rather, theset of digital action sequences 404 can include a portion of the digitalaction sequences. The number of digital action sequences included in theset of digital action sequences 404 can vary. Indeed, the bias detectionsystem 106 can determine the number of digital action sequences toinclude based on a pre-determined number of digital action sequences orbased on input received from a user (e.g., input received from anadministrator via an administrator device). In one or more embodiments,the bias detection system 106 includes one or more of the other digitalaction sequences from the digital behavior log that correspond tosessions for the task in other sets of digital action sequences (e.g.,corresponding to rolling windows of sessions for the task) as will bediscussed below with reference to FIG. 5 .

Further, in one or more embodiments, the bias detection system 106selects digital action sequences to include in the set of digital actionsequences 404 based on a chronological order. Indeed, rather thanincluding a random selection of digital action sequences from thedigital behavior log of the user in the set of digital action sequences404, the bias detection system 106 can include digital action sequencesthat chronologically precede and/or follow one another.

Though the set of digital action sequences 404 shown in FIG. 4A has beendescribed as including digital action sequences that correspond to thesame user, the bias detection system 106 can utilize sets of digitalactions sequences that include digital action sequences that correspondto different users in one or more embodiments. Indeed, the biasdetection system 106 can utilize such sets of digital actions togenerate baseline attention weights as discussed below with reference toFIG. 5 .

As shown in FIG. 4A, the bias detection system 106 utilizes theattention neural network 402 to analyze the set of digital actionsequences 404. As shown in FIG. 4A, the attention neural network 402includes an action-level encoder 406. The bias detection system 106 canutilize the action-level encoder 406 of the attention neural network 402to perform an action-level analysis of the set of digital actionsequences 404. For example, in one or more embodiments, the biasdetection system 106 utilizes the action-level encoder 406 to analyzeeach digital action of a given digital action sequence. As illustratedin FIG. 4A, the action-level encoder 406 includes a long short-termmemory (“LSTM”) layer 408 and an attention mechanism 410.

As further shown in FIG. 4A, the attention neural network 402 includes asession-level encoder 412. The bias detection system 106 can utilize thesession-level encoder 412 to perform a session-level analysis of the setof digital action sequences 404. For example, in one or moreembodiments, the bias detection system 106 utilizes the session-levelencoder 412 to analyze each digital action sequence from the set ofdigital action sequences 404 as a whole. In some instances, thesession-level encoder 412 analyzes values generated by the action-levelencoder 406. As illustrated in FIG. 4A, the session-level encoder 412includes an LSTM layer 414 and an attention mechanism 416.

Additionally, as illustrated in FIG. 4A, the attention neural network402 includes a decoder 418. The bias detection system 106 can utilizethe decoder 418 to analyze values generated by the session-level encoder412. Based on the analysis, the decoder 418 can generate a predicteddigital action sequence 424. In one or more embodiments, the predicteddigital action sequence 424 corresponds to a subsequent session for thetask that chronologically follows the digital action sequences from theset of digital action sequences 404. Indeed, the attention neuralnetwork 402 can generate the predicted digital action sequence 424 as aprediction of the sequence of digital actions the user would select fora session for the task following the set of sessions for the taskcorresponding to the set of digital action sequences 404. As illustratedin FIG. 4A, the decoder 418 includes an LSTM layer 420 and a softmaxlayer 422.

As further shown in FIG. 4A, the bias detection system 106 utilizes theattention neural network 402 to generate the attention weights 426 basedon the analysis of the set of digital action sequences 404. Inparticular, as shown, the bias detection system 106 utilizes theattention mechanism 416 of the session-level encoder 412 to generate theattention weights 426. The bias detection system 106 can further extractthe attention weights 426 from the attention neural network 402 (e.g.,from the attention mechanism 416 of the session-level encoder 412). Inone or more embodiments, the attention weights 426 indicate an extent towhich the subsequent session for the task is predicted to be influencedby the sessions corresponding to the set of digital action sequences404. The bias detection system 106 can utilize the attention weights 426to generate a visual indication of an action-selection bias of the user.

FIG. 4B illustrates a diagram providing additional detail regardingvalues generated by the various components of the attention neuralnetwork 402 in accordance with one or more embodiments. In particular,FIG. 4B illustrates the bias detection system 106 utilizing theattention neural network 402 to analyze a single set of digital actionsequences corresponding to a single set of sessions for a task togenerate a predicted digital action sequence that corresponds to asubsequent session for the task.

For example, in one or more embodiments, the bias detection system 106utilizes the attention neural network 402 to determine (e.g., predict) adigital action sequence corresponding to a session for task k at time T.To do so, the bias detection system 106 provides a set of digital actionsequences that correspond to m sessions for the task k as input to theattention neural network 402. In particular, the m sessions correspondto sessions for task k performed sequentially in time T−m,T−m+1, . . . ,T−1. In one or more embodiments, each of the digital action sequencescorresponding to the m sessions can include n digital actions. Likewise,the digital action sequence corresponding to the session for task k attime T can also include n digital actions.

To illustrate, as shown in FIG. 4B, the bias detection system 106provides, as input to the attention neural network 402, a digital actionsequence corresponding to an i-th session for task k. The digital actionsequence includes the digital actions 430 a-430 n (e.g., encodings ofthe digital actions 430 a-430 n). The bias detection system 106 utilizesthe LSTM layer 408 of the action-level encoder 406 to analyze an inputa_(i,t) and the previous hidden state h_(i,t−1) ^(a) to calculate thehidden state h_(i,t) ^(a) for the i-th session at time step t asfollows:h _(i,t) ^(a)=tan h(W _(hh) h _(i,t−1) ^(a) +W _(ah) a _(i,t))  (1)

In function (1), h_(i,t) ^(a)∈R^(n) and W_(hh) and W_(ah) representweight matrices that the attention neural network 402 learns duringanalysis of the globalized digital action sequences and/or thepersonalized digital action sequences (as discussed below with referenceto FIG. 5 ). In function (1), the superscript a indicates that thecorresponding hidden state is an action-level hidden state generated bythe action-level encoder 406.

After generating the hidden states for all digital actions of the i-thsession, the bias detection system 106 can utilize the attentionmechanism 410 of the action-level encoder 406 to generate theaction-level context vector c_(i) for the i-th input into thesession-level encoder 412 (e.g., the input corresponding to the i-thsession). For example, the bias detection system 106 can utilize theattention mechanism 410 of the action-level encoder 406 to generate theaction-level context vector c_(i) as a weighted average of the hiddenstates for the i-th session h_(i,j) ^(a) as follows:

$\begin{matrix}{c_{i} = {\sum\limits_{j = 1}^{n}\;{\alpha_{i,j}{\overset{\rightarrow}{h}}_{i,j}^{a}}}} & (2)\end{matrix}$

In function (2), α_(i,j) represents the attention weight correspondingto the i-th session and the hidden state corresponding to the j-thdigital action generated by the action-level encoder 406. In particular,α_(i,j) is an attention weight from the attention mechanism 410 of theaction-level encoder and represents the significance of the j-th digitalaction in the action-level context vector corresponding to the i-thsession. In one or more embodiments, the bias detection system 106determines the attention weight α_(i,j) as follows:

$\begin{matrix}{\alpha_{i,j} = \frac{\exp\left( e_{i,j} \right)}{\sum\limits_{k = 1}^{n}\;{\exp\mspace{14mu}\left( e_{i,k} \right)}}} & (3)\end{matrix}$

In function (3), e_(i,j) represents an alignment score between the i-thinput into the session-level encoder 412 (e.g., the input correspondingto the i-th session) and the j-th hidden state generated by theaction-level encoder 406. In one or more embodiments, e_(i,j)=a(h_(i-1)^(v),h_(j) ^(a)). In particular, the superscript v indicates that thecorresponding hidden state is a session-level hidden state generated bythe session-level encoder 412 and a( ) represents an alignment model. Inone or more embodiments, the bias detection system 106 trains thealignment model while analyzing the globalized digital action sequencesand/or the personalized digital action sequences (as discussed belowwith reference to FIG. 5 ). In particular, the bias detection system 106can train the alignment model via back-propagation of gradient from thedecoder level.

In one or more embodiments, the bias detection system 106 utilizes theaction-level encoder 406 to generate a plurality of action-level contextvectors (e.g., action-level context vectors c_(T-m) through c_(T-1))corresponding to the set of sessions using functions (1)-(3). In one ormore embodiments, the bias detection system 106 utilizes the LSTM layer414 of the session-level encoder 412 to analyze the plurality ofaction-level context vectors and generate corresponding hidden states asfollows:h _(i) ^(v)=tan h(W _(hh) h _(i-1) ^(v) +W _(vh) c _(i))  (4)

In function (4), h_(i) ^(v)∈R^(n) and represents the i-th hidden stategenerated by the session-level encoder 412. Further, W_(hh) and W_(vh)represent weight matrices that the attention neural network 402 learnsduring analysis of the globalized digital action sequences and/or thepersonalized digital action sequences (as discussed below with referenceto FIG. 5 ).

After generating the hidden states for all action-level context vectors,the bias detection system 106 can utilize the attention mechanism 416 ofthe session-level encoder 412 to generate the session-level contextvector c′_(T) for visit T as follows:

$\begin{matrix}{c_{T}^{\prime} = {\sum\limits_{i = {T - m}}^{T - 1}\;{\alpha_{i}^{\prime}{\overset{\rightarrow}{h}}_{i}^{v}}}} & (5)\end{matrix}$

In function (5), α′_(i) represents an attention weight from theattention mechanism 416 of the session-level encoder 412. In one or moreembodiments, the bias detection system 106 determines the attentionweight α′_(i) as follows:

$\begin{matrix}{\alpha_{i}^{\prime} = \frac{\exp\left( e_{pi}^{\prime} \right)}{\sum\limits_{l = {T - m}}^{T - 1}\;{\exp\mspace{14mu}\left( e_{pl}^{\prime} \right)}}} & (6)\end{matrix}$

In function (6), e′_(pi) represents an alignment score between the i-thsession and the p-th predicted digital action (e.g., generated by thedecoder 418). In one or more embodiments, e′_(pi)=a′(s_(p-1), h_(i)^(v)). In particular, a′( ) represents an alignment model. In one ormore embodiments, the bias detection system 106 trains the alignmentmodel while analyzing the globalized digital action sequences and/or thepersonalized digital action sequences (as discussed below with referenceto FIG. 5 ).

In one or more embodiments, the bias detection system 106 utilizes thedecoder 418 of the attention neural network 402 to generate a predicteddigital action sequence corresponding to a session for task k at time Tbased on the session-level context vector c′_(T). In particular, in oneor more embodiments, the bias detection system 106 utilizes the LSTMlayer 420 of the decoder 418 to generate hidden states based on thesession-level context vector c′_(T) as follows:s _(i)=ƒ(c′ _(T) ,s _(i-1) ,y _(i-1))  (7)

In function (7), s_(i) represents the hidden state corresponding to thei-th session, s_(i-1) represents the previous hidden state, and y_(i-1)represents the previous predicted digital action (e.g., the digitalaction that precedes the predicted digital action y_(i) within thepredicted digital action sequence). In one or more embodiments, thefunction ƒ( ) is a learned function. Indeed, the bias detection system106 can learn the function ƒ( ) while analyzing the globalized digitalaction sequences and/or the personalized digital action sequences (asdiscussed below with reference to FIG. 5 ).

In one or more embodiments, the bias detection system 106 furtherutilizes the softmax layer 422 of the decoder 418 to generate thepredicted digital action y_(i) based on the hidden state s_(i) asfollows:y _(i)=softmax(s _(i))  (8)

Thus, the bias detection system 106 can utilize the attention neuralnetwork 402 to generate a predicted digital action sequence thatincludes the predicted digital actions 432 a-432 n. Indeed, as mentionedabove, the predicted digital action sequence corresponds to a sessionfor task k that follows the set of sessions for task k analyzed by theattention neural network 402. In one or more embodiments, the biasdetection system 106 generates a given predicted digital action (e.g.,one of the predicted digital actions 432 a-432 n) by generating aprobability vector over the action categories discussed above withreference to FIG. 3 .

In one or more embodiments, the bias detection system 106 utilizes anattention neural network to generate the attention weights thatcorrespond to an action-selection bias of a user using a multi-phaseprocess. For example, the bias detection system 106 can utilize theattention neural network to generate a plurality of baseline attentionweights by analyzing globalized digital action sequences in a firstphase. The bias detection system 106 can further utilize the attentionneural network to adjust the baseline attention weights to generateattention weights that correspond more particularly to a user byanalyzing personalized digital action sequences in a second phase. FIGS.5A-5B illustrate block diagrams for utilizing a multi-phase process forgenerating attention weights that correspond to an action-selection biasof a user in accordance with one or more embodiments. In particular,FIG. 5A illustrates a block diagram for generating baseline attentionweights based on globalized digital action sequences. FIG. 5Billustrates a block diagram for generating attention weights thatcorrespond more particularly to a user based on personalized digitalaction sequences.

As shown in FIG. 5A, the bias detection system 106 provides globalizeddigital action sequences 502 to the attention neural network 504. In oneor more embodiments, the globalized digital action sequences 502 includedigital action sequences that correspond to sessions for a task executedby a plurality of users. For example, in one or more embodiments, theglobalized digital action sequences 502 includes a three-dimensionalmatrix with dimensions n×m×p, where n represents the total number ofanalysts with at least a threshold number of sessions for the task, mrepresents the maximum number of sessions for the task performed by then analysts, and p includes the size of the digital action sequencesconsidered (e.g., the number of digital actions in the digital actionsequences).

In some instances, the bias detection system 106 generates sets ofglobalized digital action sequences from the globalized digital actionsequences 502 where each set of globalized digital action sequencescorresponds to a particular user and a given pair of sets of globalizeddigital action sequences can correspond to multiple users. In one ormore embodiments, a set of globalized digital action sequences thatcorresponds to a user can include digital action sequences correspondingto a sequence of sessions for the task as discussed above with referenceto FIGS. 4A-4B. In some embodiments, however, each set of globalizeddigital action sequences can include digital action sequences from aplurality of users.

As further shown in FIG. 5A, the bias detection system 106 utilizes theattention neural network 504 to generate a predicted digital actionsequence 506. Indeed, the bias detection system 106 can utilize theattention neural network 504 to generate the predicted digital actionsequence 506 as discussed above with reference to FIGS. 4A-4B. Forexample, the bias detection system 106 can utilize the attention neuralnetwork 504 to analyze a set of globalized digital action sequences fromthe globalized digital action sequences 502 and generate the predicteddigital action sequence 506 based on the analysis. In one or moreembodiments, the predicted digital action sequence 506 corresponds to asession for the task executed by the user that follows the set ofsessions for the task corresponding to the set of globalized digitalaction sequences.

Additionally, as shown in FIG. 5A, the bias detection system 106compares the predicted digital action sequence 506 with an observeddigital action sequence 510 using a loss function 508. In one or moreembodiments, the observed digital action sequence 510 corresponds to thesession for the task executed by the user that follows the set ofsessions for the task corresponding to the set of globalized digitalaction sequences. In other words, the set of globalized digital actionsequences and the predicted digital action sequence 506 can correspondto sessions for the task executed by the same user (e.g., identifiedfrom the digital behavior log associated with the same user).

In one or more embodiments, the loss function 508 includes across-entropy loss function, though the loss function 508 can includevarious other applicable losses. As mentioned above, a given predicteddigital action can include a probability vector that includes aplurality of probabilities over the available action categories.Accordingly, in one or more embodiments, the bias detection system 106takes the cross-entropy loss for all of the probabilities within theprobability vector.

As shown in FIG. 5A, the bias detection system 106 back propagates thedetermined loss to the attention neural network 504 (as indicated by thedashed line 512) to optimize the model by updating itsparameters/weights. For example, in one or more embodiments, the biasdetection system 106 averages the cross-entropy loss function across theglobalized digital action sequences 502 and back propagates thedetermined average to update the weights of the LSTM layers andattention mechanisms of the attention neural network 504. Consequently,with each iteration of training, the bias detection system 106 graduallyimproves the accuracy (e.g., minimizes the loss) with which theattention neural network 504 can predict the digital action sequence{{y_(i)}_(i=1) ^(n)}_(T) from the sequence of sessions V_(T-m) toV_(T-1). Accordingly, the bias detection system 106 can utilize theattention neural network 504 to generate the baseline attention weights514 based on the globalized digital action sequences 502.

As shown in FIG. 5B, the bias detection system 106 provides personalizeddigital action sequences 520 to the attention neural network 504. Inparticular, the personalized digital action sequences 520 can includedigital action sequences that correspond to sessions for the taskexecuted by the same user. In some instances, the bias detection system106 generates sets of personalized digital action sequences from thepersonalized digital action sequences 520. In one or more embodiments,the sets of personalized digital action sequences include rollingwindows of sessions for the task.

As shown in FIG. 5B, the bias detection system 106 utilizes theattention neural network 504 to analyze a given set of personalizeddigital action sequences and generate a predicted digital actionsequence 522. In particular, as shown, the attention neural network 504analyzes the given set of personalized digital action sequences usingthe baseline attention weights 514 generated as discussed above withreference to FIG. 5A. In other words, the attention neural network 504begins to analyze the personalized digital action sequences 520 usingthe baseline attention weights 514—before the bias detection system 106updates the baseline attention weights 514. In one or more embodiments,the predicted digital action sequence 522 corresponds to a subsequentsession for the task (e.g., a session for the task that chronologicallyfollows the sessions for the task represented in the set of personalizeddigital action sequences).

Additionally, as shown in FIG. 5B, the bias detection system 106compares the predicted digital action sequence 522 with an observeddigital action sequence 524 using the loss function 508. In one or moreembodiments, the observed digital action sequence 524 corresponds to thesession for the task executed by the user that follows the set ofsessions for the task corresponding to the set of personalized digitalaction sequences. Further, the bias detection system 106 back propagatesthe determined loss to the attention neural network 504 (as indicated bythe dashed line 526) to optimize the model by updating itsparameters/weights. For example, in one or more embodiments, the biasdetection system 106 averages the cross-entropy loss function across thepersonalized digital action sequences 520 and back propagates thedetermined average to update the weights of the LSTM layers andattention mechanisms of the attention neural network 504. Consequently,with each iteration of training, the bias detection system 106 graduallyimproves the accuracy with which the attention neural network 504 canpredict digital action sequences.

Accordingly, the bias detection system 106 can utilize the attentionneural network 504 to generate the attention weights 528 that correspondmore particularly to a given user based on rolling windows of sessionscorresponding to the personalized digital action sequences 520. Theattention weights 528 further correspond to and indicate anaction-selection bias of the user. Indeed, the attention weights canindicate, where the user has previously executed a task multiple timesin the past, whether the user will rely more on the older sessions orthe more recent sessions when selecting a digital action sequence toexecute the task in a future session.

To give an example of generating the attention weights based on arolling windows of sessions, the personalized digital action sequences520 can include ten digital action sequences corresponding to tensessions for the task executed by the user. The bias detection system106 can generate a first window of sessions (e.g., a first set ofsessions) that includes sessions one through six (e.g., using a windowsize of six). The bias detection system 106 can further generate asecond window of sessions that includes sessions two through seven, athird window of sessions that includes sessions three through eight, anda fourth window of sessions that includes sessions four through nine.The bias detection system 106 can utilize the attention neural network504 to analyze the first window of sessions and generate a predicteddigital action sequence that corresponds to the session seven (e.g., thebias detection system 106 predicts what digital action sequence would beincluded in the session seven). The bias detection system 106 furtheruses a loss function to compare the predicted digital action sequence toan observed digital action sequence that corresponds to session seven(e.g., the bias detection system 106 compares the predicted digitalaction sequence to session seven). The bias detection system 106 backpropagates the determined loss to the attention neural network 504 toadjust or update its weights. The bias detection system 106 canreiterate the process using the second, third, and fourth windows ofsessions, and thus utilize rolling windows of sessions to generate theattention weights 528 corresponding to the user.

The bias detection system 106 can utilize various other methods ofgenerating session weights. For example, the bias detection system 106can (e.g., utilizing the machine learning model 204 or, morespecifically, the attention neural network 504) generate session weightsbased on the frequencies of digital actions associated with the digitalaction sequences. In particular, the bias detection system 106 cangenerate a predicted digital action sequence based on the frequency ofdigital actions associated with one or more previous sessions of a task.The bias detection system 106 can further update session weights basedon comparing the predicted digital action sequence with a correspondingground truth. Accordingly, the session weights can indicate an extentthat a future session for the task relies on digital actions selected inprevious sessions based on the frequencies of the digital actionsselected in those previous sessions.

In one or more embodiments, the bias detection system 106 can apply theaction-selection bias determined by analyzing sessions for one task toanother task. In other words, the bias detection system 106 candetermine that the action-selection bias indicated by the sessionweights generated for a user is more generally indicative of anaction-selection bias that affects how that user selects digital actionsequences for a variety of tasks.

Thus, the bias detection system 106 can utilize a machine learning modelto generate sessions weights that correspond to an action-selection biasof a user. Accordingly, the algorithm and acts described with referenceto FIG. 5B can comprise the corresponding structure for performing astep for determining session weights for the sessions utilizing amachine learning model. Additionally, the attention neural networkarchitecture described with reference to FIGS. 4A-4B can comprise themachine learning model in a step for determining session weights for thesessions utilizing a machine learning model.

As mentioned above, the bias detection system 106 can generate a visualindicator of an action-selection bias of a user. The bias detectionsystem 106 can generate various different visual indicators. The biasdetection system 106 can further provide the visual indicator fordisplay on a graphical user interface. FIGS. 6A-7 illustrate graphicaluser interfaces used by the bias detection system 106 to display avisual indicator of an action-selection bias of a user in accordancewith one or more embodiments.

In one or more embodiments, the bias detection system 106 provides, fordisplay on a graphical user interface, visual indications ofaction-selection biases that correspond to a plurality of users. Forexample, FIG. 6A illustrates a graphical user interface 600 used by thebias detection system 106 to display graphical representations 604 a-604f of sets of session weights corresponding to a set of users on a clientdevice 602 in accordance with one or more embodiments. As shown in FIG.6A, the bias detection system 106 presents the graphical representations604 a-604 f in a graph 606 displayed within the graphical user interface600. Indeed, each of the graphical representations 604-604 f depicts“session weights” for a range of “session numbers.” In one or moreembodiments, the range of “session numbers” corresponds to the windowsize used by the bias detection system 106 to analyze the rollingwindows of sessions utilizing the machine learning model to generate thesessions weights. For example, as shown in FIG. 6A, the graphicalrepresentations 604 a-604 f provide sessions weights across sixdifferent session numbers.

In one or more embodiments, the graphical representations 604 a-604 fdirectly correspond to the session weights generated by the machinelearning model after analyzing the globalized digital action sequencesand the personalized digital action sequences that correspond to theuser. In other words, the graphical representations 604 a-604 frepresent the user-specific session weights that were generated on topof the baseline session weights. In some instances, however, the biasdetection system 106 determines the difference between the user-specificsession weights for each corresponding user and the baseline sessionweights and generates the graphical representations 604 a-604 f based onthe determined differences.

To provide examples of action-selection bias representation, thegraphical representation 604 a indicates a recency bias of thecorresponding user. Indeed, as shown in FIG. 6A, the session weights ofthe graphical representation 604 a increase across the session numbers,indicating that the corresponding user selects digital actions thatalign with the digital action sequences selected for more recentsessions. On the other hand, the graphical representation 604 findicates an anchoring bias of the corresponding user. As shown, thesession weights of the graphical representation 604 f decrease acrossthe session numbers, indicating that the corresponding user selectsdigital actions that align with the digital action sequences selectedfor earlier sessions. In one or more embodiments, the graphicalrepresentation 604 e indicates that the corresponding user does not havean action-selection bias, as the session weights vary only slightlyacross the session numbers. In some embodiments, however, the graphicalrepresentation 604 e is indicative of strong anchoring bias, showingthat the corresponding user has a long-running habit of selectingdigital actions in accordance with early sessions.

In some embodiments, the bias detection system 106 provides, for displayon a graphical user interface, a visual indication of anaction-selection bias that corresponds to a single user. In someinstances, the bias detection system 106 provides a visual indicationcorresponding to a single user in response to receiving a userinteraction with one of the graphical representations 604 a-604 fpresented within the graphical user interface 600. To illustrate, inresponse to receiving a user interaction with a graphical representationthat corresponds to a particular user, the bias detection system 106 canprovide, for display, one or more additional visual indications of theaction-selection bias of that user (e.g., without the visual indicationsof the other users). In some embodiments, however, the bias detectionsystem 106 provides the visual indication(s) of the action-selectionbias of the user automatically and initially (e.g., without firstproviding a visual indication as part of the graphical representations604 a-604 f).

The bias detection system 106 can generate and provide various visualindications of an action-selection bias of a single user. For example,FIG. 6B illustrates a graphical user interface 610 used by the biasdetection system 106 to display a graphical representation 614 ofsession weights corresponding to a user on a client device 612 inaccordance with one or more embodiments. In particular, the biasdetection system 106 can generate the graphical representation 614 torepresent changes to the attention weights across a set of rollingwindows of sessions for the task that correspond to the user. Indeed,the bias detection system 106 can maintain the sessions weights for theuser that resulted from analyzing each set of personalized digitalaction sequences (e.g., each window) for the user. Thus, the graphicalrepresentation 614 presents a three-dimensional graph that shows how the“session weights” for the “session numbers” changed across the various“rolling windows.”

In one or more embodiments, the bias detection system 106 provides thegraphical representation 614 for display as part of an animation.Indeed, the bias detection system 106 can provide an animation fordisplay within the graphical user interface 610 that initially shows thesession weights for the sessions numbers determined after analyzing thefirst window and then progressively adds the session weights determinedafter analyzing the subsequent windows.

FIG. 6C illustrates another graphical user interface 620 used by thebias detection system 106 to display a graphical representation 624 offrequencies of digital actions selected by the user across the sessionsfor the task on a client device 622 in accordance with one or moreembodiments. For example, as shown in FIG. 6C, the graphicalrepresentation 624 shows a plurality of frequency lines where eachfrequency line corresponds to a session for the task—with twenty-twosessions being represented in the graphical representation. Eachfrequency line shows, for a plurality of available digital actions, thefrequency of digital actions selected for the corresponding session forthe task. In some embodiments, rather than showing frequencies, the biasdetection system 106 generates the graphical representation 624 to showa total number of times a given digital action was selected in a givensession for the task.

As further shown in FIG. 6A, the bias detection system 106 can providefor display, in association with the graphical representation 624, agraphical representation 626. In particular, the graphicalrepresentation 626 can include graphical representations of the sessionweights determined after each session for the task represented in thegraphical representation 624, including a graphical representation ofthe session weights determined after the final session for the taskexecuted by the user (e.g., represented by the bold line). As shown, thegraphical representation 624 indicates an anchoring bias of thecorresponding user as the session weights decrease across therepresented session numbers.

In one or more embodiments, the bias detection system 106 provides thegraphical representations 624, 626 for display as part of an animation.Indeed, the bias detection system 106 can provide an animation fordisplay within the graphical user interface 620 that initially shows(e.g., within the graphical representation 624) the frequencies ofdigital actions selected during the first session for the task and shows(e.g., within the graphical representation 626) the session weightsdetermined after analyzing the first session for the task (or a windowthat includes the first session for the task). The bias detection system106 can animate the graphical representations 624, 626 to progressivelyadd, respectively, the frequencies of digital actions for eachsubsequent session and the adjusted session weights after analyzing thesubsequent session.

FIG. 6D illustrates a graphical user interface 630 used by the biasdetection system 106 to display a frequency heat map 634 indicatingfrequencies of digital actions selected by the user across sessions fora task on a client device 632 in accordance with one or moreembodiments. As shown in FIG. 6D, the bias detection system 106 forprovides, for display within the graphical user interface 630, afrequency key 636 that provides an association between a given frequencyand a color (or hue, shade, etc.) that is presented within the frequencyheat map 634.

FIG. 7 illustrates a graphical user interface 700 used by the biasdetection system 106 to display a visual indication 706 of anaction-selection bias of a user on a client device 702 based on the userselecting digital actions consistent with the action-selection bias inaccordance with one or more embodiments. For example, as shown in FIG. 7, the graphical user interface 700 includes a plurality of digitalactions 704 a-704 d that can be selected by a user. Though FIG. 7illustrates a particular number of digital actions, it should be notedthat the graphical user interface 700 can include additional digitalactions that are not shown.

The bias detection system 106 can receive indications of selections ofone or more digital actions as the user selects digital actions for asession for a task. In one or more embodiments, the bias detectionsystem 106 tracks the sequence of digital actions selected by the user.Accordingly, the bias detection system 106 can determine that thesequence of digital actions selected by the user for the session for thetask are associated with the action-selection bias of the user. Forexample, the bias detection system 106 can determine that the sequenceof digital actions selected by the user up to a certain point in timecorrespond to one or more digital action sequences that the user waspredicted to rely on consistent with the determined action-selectionbias of the user. In one or more embodiments, the bias detection system106 determines that the user is selecting a digital action sequenceconsistent with an action-selection bias after the user has selected athreshold number of digital actions that are consistent with theaction-selection bias of the user.

In response to determining that the user is selecting digital actionsconsistent with the action-selection bias of the user, the biasdetection system 106 can generate and provide the visual indication 706of the action-selection bias for display within the graphical userinterface 700. In particular, the visual indication 706 can include anotification that the user is acting consistent with the determinedaction-selection bias of the user. In some embodiments, the visualindication 706 can further include direction for the user to selectdifferent digital actions to obtain different results and/or obtain newinsights.

In one or more embodiments, the bias detection system 106 provides thevisual indication 706 via an intelligent agent, such as a virtualassistant. Indeed, in some embodiments, the bias detection system 106provides an intelligent agent that can provide communications (e.g.,answer questions, provide tips) to the user through the graphical userinterface. The bias detection system 106 can utilize the intelligentagent to notify the user when the user is selecting digital actionsconsistent with an action-selection bias.

Thus, the bias detection system 106 introduces a previously-unused andunconventional approach for identifying a bias associated with a userthat influences how that user selects digital action sequences toexecute a task. Indeed, the bias detection system 106 implements anunconventional ordered combination of steps to analyze digital actionsequences corresponding to previous sessions for a task executed by auser, generate session weights that indicate an action-selection bias ofthe user based on the analysis using a machine learning model, andproviding a visual indication of the action-selection bias of the userfor display on a graphical user interface. Thus, the bias detectionsystem 106 can inform uses of their action-selection biases andencourage the users to explore other options for executing a task.Further, by utilizing a machine learning model to generate the sessionweights that are indicative of an action-selection bias, the biasdetection system 106 generates values that could not be determined byhumans.

By determining action-selection biases of users, the bias detectionsystem 106 can operate more flexibly than conventional systems. Indeed,as previously mentioned, conventional systems were often limited todetecting biases that were inherent to the digital data that to beanalyzed or the models used to perform the analysis, failing to accountfor user-specific biases. The bias detection system 106 flexiblyidentifies biases that are specific to the way users select digitalaction for task execution. Further, by utilizing a machine learningmodel to generate the sessions weights upon which the visual indicationof the action-selection bias is based, the bias detection system 106provides improved scalability with regard to the identification of suchbiases compared to previous efforts.

Turning now to FIG. 8 , additional detail will be provided regardingvarious components and capabilities of the bias detection system 106. Inparticular, FIG. 8 illustrates the bias detection system 106 implementedby the computing device 800 (e.g., the server(s) 102, the administratordevice 110, and/or one of the client devices 114 a-114 n as discussedabove with reference to FIG. 1 ). Additionally, the bias detectionsystem 106 is also part of the analytics system 104. As shown, the biasdetection system 106 can include, but is not limited to, a digitalaction encoding manager 802, a digital action sequence selection manager804, a machine learning model application manager 806, a session weightsgenerator 808, an action-selection bias visual indication generator 810,a graphical user interface manager 812, and data storage 814 (whichincludes digital behavior logs 816, machine learning model 818, andsession weights 820).

As just mentioned, and as illustrated in FIG. 8 , the bias detectionsystem 106 includes the digital action encoding manager 802. Inparticular, the digital action encoding manager 802 can encode thedigital actions of a digital behavior log corresponding to a user. Forexample, the digital action encoding manager 802 can generate an encodeddigital behavior log corresponding to the digital behavior log of a userby encoding the digital actions stored in the digital behavior log. Inone or more embodiments, the digital action encoding manager 802 encodesthe digital actions by assigning the digital actions to an actioncategory.

Further, as shown in FIG. 8 , the bias detection system 106 includes thedigital action sequence selection manager 804. In particular, thedigital action sequence selection manager 804 can select digital actionsequences from the digital behavior log of a user (e.g., the encodeddigital behavior log generated by the digital action encoding manager802). For example, the digital action sequence selection manager 804 canidentify, from the digital behavior log of the user, a task-identifyingdigital action. The digital action sequence selection manager 804 canfurther select a first subset of digital actions that chronologicallyprecedes the task-identifying digital action and is within a thresholdnumber of digital actions from the task-identifying digital action aswell as a second subset of digital actions that chronologically followsthe task-identifying digital action and is within the threshold numberof digital actions from the task-identifying digital action.

Additionally, as shown in FIG. 8 , the bias detection system 106includes the machine learning model application manager 806. Inparticular, the machine learning model application manager 806 canutilize a machine learning model to analyze digital action sequences,such as those identified by the digital action sequence selectionmanager 804.

As shown in FIG. 8 , the bias detection system 106 further includes thesession weights generator 808. In particular, the session weightsgenerator 808 can operate in conjunction with the machine learning modelapplication manager 806 to generate session weights that are associatedwith an action-selection bias of a user. For example, the sessionweights generator 808 can operate in conjunction with the machinelearning model application manager 806 to incorporate a multi-phaseprocess for generating session weights using globalized digital actionsequences and personalized digital action sequences. The session weightsgenerator 808 can adjust the session weights of the machine learningmodel used by the machine learning model application manager 806 togenerate session weights that indicate an action-selection bias of auser.

As shown in FIG. 8 , the bias detection system 106 also includes theaction-selection bias visual indication generator 810. In particular,the action-selection bias visual indication generator 810 can generate avisual indication of an action-selection bias of a user. For example,the action-selection bias visual indication generator 810 can generategraphical representations of the session weights generated by thesession weights generator 808, graphical representations of frequenciesof digital actions selected by the user (e.g., a frequency heat map), orother visual indications to be provided for display via a graphical userinterface.

Additionally, as shown in FIG. 8 , the bias detection system 106includes the graphical user interface manager 812. In particular, thegraphical user interface manager 812 can provide graphical components,such as a visual indication of an action-selection bias of a user, fordisplay on a graphical user interface. The graphical user interfacemanager 812 can further receive indications of user selections, such asan indication of a user selection of a graphical representation ofsession weights that correspond to a particular user or indications ofuser selections of digital actions for a session for a task. In one ormore embodiments, in response to receiving an indication of a userselection, the graphical user interface manager 812 can provideadditional information and/or visual indications, such as an indicationthat digital actions selected by a user are associated with a determinedaction-selection bias of the user.

As further shown in FIG. 8 , the bias detection system 106 includes datastorage 814. In particular, data storage 814 includes digital behaviorlogs 816, the machine learning model 818, and session weights 820.Digital behavior logs 816 can store the digital behavior logs of aplurality of users. The machine learning model 818 can store the machinelearning model used by the machine learning model application manager806 and the session weights generator 808 to analyze digital actionsequences and generate session weights, respectively. For example, themachine learning model 818 can store an attention neural network used toanalyze digital action sequences and generate attention weights. Thesession weights 820 can include the session weights generated by thesession weights generator 808.

Each of the components 802-820 of the bias detection system 106 caninclude software, hardware, or both. For example, the components 802-820can include one or more instructions stored on a computer-readablestorage medium and executable by processors of one or more computingdevices, such as a client device or server device. When executed by theone or more processors, the computer-executable instructions of the biasdetection system 106 can cause the computing device(s) to perform themethods described herein. Alternatively, the components 802-820 caninclude hardware, such as a special-purpose processing device to performa certain function or group of functions. Alternatively, the components802-820 of the bias detection system 106 can include a combination ofcomputer-executable instructions and hardware.

Furthermore, the components 802-820 of the bias detection system 106may, for example, be implemented as one or more operating systems, asone or more stand-alone applications, as one or more modules of anapplication, as one or more plug-ins, as one or more library functionsor functions that may be called by other applications, and/or as acloud-computing model. Thus, the components 802-820 of the biasdetection system 106 may be implemented as a stand-alone application,such as a desktop or mobile application. Furthermore, the components802-820 of the bias detection system 106 may be implemented as one ormore web-based applications hosted on a remote server. Alternatively, oradditionally, the components 802-820 of the bias detection system 106may be implemented in a suite of mobile device applications or “apps.”For example, in one or more embodiments, the bias detection system 106can comprise or operate in connection with digital software applicationssuch as ADOBE® ANALYTICS or ADOBE® EXPERIENCE CLOUD®. “ADOBE” and“EXPERIENCE CLOUD” are either registered trademarks or trademarks ofAdobe Inc. in the United States and/or other countries.

FIGS. 1-8 , the corresponding text and the examples provide a number ofdifferent methods, systems, devices, and non-transitorycomputer-readable media of the bias detection system 106. In addition tothe foregoing, one or more embodiments can also be described in terms offlowcharts comprising acts for accomplishing the particular results asshown in FIG. 9 . FIG. 9 may be performed with more or fewer acts.Further, the acts may be performed in different orders. Additionally,the acts described herein may be repeated or performed in parallel withone another or in parallel with different instances of the same orsimilar acts.

As mentioned, FIG. 9 illustrates a flowchart of a series of acts 900 forgenerating a visual indication of an action-selection bias of a user inaccordance with one or more embodiments. While FIG. 9 illustrates actsaccording to one embodiment, alternative embodiments may omit, add to,reorder, and/or modify any of the acts shown in FIG. 9 . The acts ofFIG. 9 can be performed as part of a method. For example, in someembodiments, the acts of FIG. 9 can be performed as part of acomputer-implemented method for determining biases in task actions.Alternatively, a non-transitory computer-readable medium can storeinstructions thereon that, when executed by at least one processor,cause a computing device to perform the acts of FIG. 9 . In someembodiments, a system can perform the acts of FIG. 9 . For example, inone or more embodiments, a system includes at least one memory devicecomprising a digital behavior log for one or more tasks executed by auser and an attention neural network. The system can further include atleast one server device configured to cause the system to perform theacts of FIG. 9 .

The series of acts 900 includes an act 902 of identifying digital actionsequences associated with a user. For example, the act 902 can involveidentifying, from a digital behavior log corresponding to a user, a setof digital action sequences corresponding to sessions for a taskexecuted by the user. In particular, the act 902 can involveidentifying, from a digital behavior log corresponding to a user, a setof digital action sequences corresponding to a set of sessions for atask executed by the user.

In one or more embodiments, identifying the set of digital actionsequences corresponding to the sessions for the task includeidentifying, within the digital behavior log corresponding to the user,a set of task-identifying digital actions that correspond to the task;and selecting, from within the digital behavior log, a set of digitalactions from within a threshold number of digital actions of eachtask-identifying digital action.

To illustrate, in one or more embodiments, identifying the set ofdigital action sequences corresponding to the set of sessions for thetask comprises, for a session for the task executed by the user:identifying, within the digital behavior log corresponding to the user,a task-identifying digital action corresponding to the task; selecting,from within the digital behavior log, a first subset of digital actionsthat chronologically precedes the task-identifying digital action and iswithin a threshold number of digital actions from the task-identifyingdigital action; and selecting, from within the digital behavior log, asecond subset of digital actions that chronologically follows thetask-identifying digital action and is within the threshold number ofdigital actions from the task-identifying digital action.

The series of acts 900 also includes an act 904 of generating sessionweights utilizing a machine learning model. For example, the act 904 caninvolve generating, utilizing a machine learning model, session weightsindicating an extent a future session for the task is predicted to beinfluenced by the set of sessions. In some instances, generating thesession weights comprises generating, utilizing the machine learningmodel, the session weights indicating an extent the future session forthe task is predicted to be influenced by a frequency of digital actionswithin one or more of the set of sessions. In one or more embodiments,generating the session weights utilizing the machine learning modelcomprises generating, utilizing the machine learning model, a predicteddigital action sequence corresponding to the future session for thetask; and generating the session weights based on a comparison of thepredicted digital action sequence with an observed digital actionsequence corresponding to the future session for the task. In someembodiments, the bias detection system 106 further generates the sessionweights utilizing the machine learning model by identifying, within thedigital behavior log, a subsequent future session for the task among aset of rolling windows of sessions that progressively follow the set ofsessions for the task; generating, utilizing the machine learning model,an additional predicted digital action sequence for the taskcorresponding to the subsequent future session for the task; andadjusting the session weights based on a comparison of the additionalpredicted digital action sequence with an additional observed digitalaction sequence corresponding to the subsequent future session for thetask.

Further, the series of acts 900 includes an act 906 of providing avisual indication of an action-selection bias of a user. For example,the act 906 can involve providing, for display on a graphical userinterface, a visual indication of an action-selection bias of the userfor the task based on the session weights. In other words, the biasdetection system 106 can generate a graphical user interface comprisinga visual indication of an action-selection bias corresponding to theuser for the task based on the session weights. In one or moreembodiments, the visual indication of the action-selection biascorresponding to the user for the task comprises a graphicalrepresentation of the session weights.

In one or more embodiments, providing the visual indication of theaction-selection bias of the user comprises generating a graphicalindication of an anchoring bias by which an earlier session for the taskinfluences a digital action sequence in the future session for the taskmore than a later session for the task. In some embodiments, providingthe visual indication of the action-selection bias of the user comprisesgenerating a graphical indication of a recency bias by which a latersession for the task influences a digital action sequence in the futuresession for the task more than an earlier session for the task. In someinstances, providing the visual indication of the action-selection biasof the user comprises generating a graphical representation of thesession weights.

In one or more embodiments, the bias detection system 106 receives, froma client device associated with the user, one or more digital actionscorresponding to a session for the task; determines that the one or moredigital actions are associated with the action-selection bias of theuser; and provides the visual indication of the action-selection bias ofthe user based on determining that the one or more digital actions areassociated with the action-selection bias of the user.

In some embodiments, the bias detection system 106 provides, for displayon the graphical user interface, the visual indication of theaction-selection bias of the user for the task with a set of visualindications of action-selection biases of a set of users; receives, viathe graphical user interface, a user selection of the visual indicationof the action-selection bias of the user; and provides, for display onthe graphical user interface, a graphical representation of frequenciesof digital actions selected by the user across the set of sessions.

In one or more embodiments, the series of acts 900 includes acts formore particularly generating a visual indication of an action-selectionbias of a user based on attention weights generated using an attentionneural network. For example, in one or more embodiments, the actsinclude identifying, from the digital behavior log, a set of digitalaction sequences corresponding to a set of sessions for a task from theone or more tasks; generating, utilizing the attention neural network, apredicted digital action sequence corresponding to a subsequent sessionfor the task; extracting, from the attention neural network, attentionweights indicating an extent the subsequent session for the task ispredicted to be influenced by the set of sessions; and providing, fordisplay on a graphical user interface, a graphical representation of theattention weights depicting an action-selection bias of the user for thetask.

In one or more embodiments, the bias detection system 106 generates thepredicted digital action sequence corresponding to the subsequentsession for the task by generating, utilizing an action-level encoder ofthe attention neural network, a set of action-level context vectorscorresponding to the set of sessions; generating, utilizing asession-level encoder of the attention neural network, a session-levelcontext vector based on the set of action-level context vectors; andgenerating, utilizing a decoder of the attention neural network, thepredicted digital action sequence based on the session-level contextvector. In some embodiments, the bias detection system 106 furtheridentifies a set of attention weights corresponding to an attentionmechanism of the session-level encoder; adjusts the set of attentionweights based on a comparison of the predicted digital action sequencewith an observed digital action sequence corresponding to the subsequentsession for the task; and extracts the attention weights by extractingthe adjusted set of attention weights from the attention mechanism ofthe session-level encoder.

In some embodiments, the bias detection system 106 generates, utilizingthe attention neural network, baseline attention weights based ondigital action sequences corresponding to sessions for the task executedby a set of users; and generates, utilizing the attention neuralnetwork, the attention weights based on the baseline attention weightsand the set of digital action sequences corresponding to the user.

In one or more embodiments, providing the graphical representation ofthe attention weights can include generating a graphical representationof an anchoring bias by which earlier sessions for the task areassociated with greater attention weights than later sessions for thetask. In some embodiments, providing the graphical representation of theattention weights includes generating a graphical representation of arecency bias by which later sessions for the task are associated withgreater attention weights than earlier sessions for the task. In someinstances, providing the graphical representation of the attentionweights includes generating a graphical representation of changes to theattention weights across a set of rolling windows of sessions for thetask that correspond to the user.

In one or more embodiments, the bias detection system 106 provides, fordisplay on the graphical user interface, the graphical representation ofthe attention weights depicting the action-selection bias of the usertogether with graphical representations of sets of attention weightscorresponding to a set of users; receives, via the graphical userinterface, a user selection of the graphical representation of theattention weights depicting the action-selection bias of the user; andprovides, for display on the graphical user interface, a frequency heatmap indicating frequencies of digital actions selected by the useracross the set of sessions.

Embodiments of the present disclosure may comprise or utilize a specialpurpose or general-purpose computer including computer hardware, suchas, for example, one or more processors and system memory, as discussedin greater detail below. Embodiments within the scope of the presentdisclosure also include physical and other computer-readable media forcarrying or storing computer-executable instructions and/or datastructures. In particular, one or more of the processes described hereinmay be implemented at least in part as instructions embodied in anon-transitory computer-readable medium and executable by one or morecomputing devices (e.g., any of the media content access devicesdescribed herein). In general, a processor (e.g., a microprocessor)receives instructions, from a non-transitory computer-readable medium,(e.g., a memory), and executes those instructions, thereby performingone or more processes, including one or more of the processes describedherein.

Computer-readable media can be any available media that can be accessedby a general purpose or special purpose computer system.Computer-readable media that store computer-executable instructions arenon-transitory computer-readable storage media (devices).Computer-readable media that carry computer-executable instructions aretransmission media. Thus, by way of example, and not limitation,embodiments of the disclosure can comprise at least two distinctlydifferent kinds of computer-readable media: non-transitorycomputer-readable storage media (devices) and transmission media.

Non-transitory computer-readable storage media (devices) includes RAM,ROM, EEPROM, CD-ROM, solid state drives (“SSDs”) (e.g., based on RAM),Flash memory, phase-change memory (“PCM”), other types of memory, otheroptical disk storage, magnetic disk storage or other magnetic storagedevices, or any other medium which can be used to store desired programcode means in the form of computer-executable instructions or datastructures and which can be accessed by a general purpose or specialpurpose computer.

A “network” is defined as one or more data links that enable thetransport of electronic data between computer systems and/or modulesand/or other electronic devices. When information is transferred orprovided over a network or another communications connection (eitherhardwired, wireless, or a combination of hardwired or wireless) to acomputer, the computer properly views the connection as a transmissionmedium. Transmissions media can include a network and/or data linkswhich can be used to carry desired program code means in the form ofcomputer-executable instructions or data structures and which can beaccessed by a general purpose or special purpose computer. Combinationsof the above should also be included within the scope ofcomputer-readable media.

Further, upon reaching various computer system components, program codemeans in the form of computer-executable instructions or data structurescan be transferred automatically from transmission media tonon-transitory computer-readable storage media (devices) (or viceversa). For example, computer-executable instructions or data structuresreceived over a network or data link can be buffered in RAM within anetwork interface module (e.g., a “NIC”), and then eventuallytransferred to computer system RAM and/or to less volatile computerstorage media (devices) at a computer system. Thus, it should beunderstood that non-transitory computer-readable storage media (devices)can be included in computer system components that also (or evenprimarily) utilize transmission media.

Computer-executable instructions comprise, for example, instructions anddata which, when executed by a processor, cause a general-purposecomputer, special purpose computer, or special purpose processing deviceto perform a certain function or group of functions. In someembodiments, computer-executable instructions are executed on ageneral-purpose computer to turn the general-purpose computer into aspecial purpose computer implementing elements of the disclosure. Thecomputer executable instructions may be, for example, binaries,intermediate format instructions such as assembly language, or evensource code. Although the subject matter has been described in languagespecific to structural features and/or methodological acts, it is to beunderstood that the subject matter defined in the appended claims is notnecessarily limited to the described features or acts described above.Rather, the described features and acts are disclosed as example formsof implementing the claims.

Those skilled in the art will appreciate that the disclosure may bepracticed in network computing environments with many types of computersystem configurations, including, personal computers, desktop computers,laptop computers, message processors, hand-held devices, multiprocessorsystems, microprocessor-based or programmable consumer electronics,network PCs, minicomputers, mainframe computers, mobile telephones,PDAs, tablets, pagers, routers, switches, and the like. The disclosuremay also be practiced in distributed system environments where local andremote computer systems, which are linked (either by hardwired datalinks, wireless data links, or by a combination of hardwired andwireless data links) through a network, both perform tasks. In adistributed system environment, program modules may be located in bothlocal and remote memory storage devices.

Embodiments of the present disclosure can also be implemented in cloudcomputing environments. In this description, “cloud computing” isdefined as a model for enabling on-demand network access to a sharedpool of configurable computing resources. For example, cloud computingcan be employed in the marketplace to offer ubiquitous and convenienton-demand access to the shared pool of configurable computing resources.The shared pool of configurable computing resources can be rapidlyprovisioned via virtualization and released with low management effortor service provider interaction, and then scaled accordingly.

A cloud-computing model can be composed of various characteristics suchas, for example, on-demand self-service, broad network access, resourcepooling, rapid elasticity, measured service, and so forth. Acloud-computing model can also expose various service models, such as,for example, Software as a Service (“SaaS”), Platform as a Service(“PaaS”), and Infrastructure as a Service (“IaaS”). A cloud-computingmodel can also be deployed using different deployment models such asprivate cloud, community cloud, public cloud, hybrid cloud, and soforth. In this description and in the claims, a “cloud-computingenvironment” is an environment in which cloud computing is employed.

FIG. 10 illustrates a block diagram of an example computing device 1000that may be configured to perform one or more of the processes describedabove. One will appreciate that one or more computing devices, such asthe computing device 1000 may represent the computing devices describedabove (e.g., the server(s) 102, the administrator device 110, and/or theclient devices 114 a-114 n). In one or more embodiments, the computingdevice 1000 may be a mobile device (e.g., a mobile telephone, asmartphone, a PDA, a tablet, a laptop, a camera, a tracker, a watch, awearable device). In some embodiments, the computing device 1000 may bea non-mobile device (e.g., a desktop computer or another type of clientdevice). Further, the computing device 1000 may be a server device thatincludes cloud-based processing and storage capabilities.

As shown in FIG. 10 , the computing device 1000 can include one or moreprocessor(s) 1002, memory 1004, a storage device 1006, input/outputinterfaces 1008 (or “I/O interfaces 1008”), and a communicationinterface 1010, which may be communicatively coupled by way of acommunication infrastructure (e.g., bus 1012). While the computingdevice 1000 is shown in FIG. 10 , the components illustrated in FIG. 10are not intended to be limiting. Additional or alternative componentsmay be used in other embodiments. Furthermore, in certain embodiments,the computing device 1000 includes fewer components than those shown inFIG. 10 . Components of the computing device 1000 shown in FIG. 10 willnow be described in additional detail.

In particular embodiments, the processor(s) 1002 includes hardware forexecuting instructions, such as those making up a computer program. Asan example, and not by way of limitation, to execute instructions, theprocessor(s) 1002 may retrieve (or fetch) the instructions from aninternal register, an internal cache, memory 1004, or a storage device1006 and decode and execute them.

The computing device 1000 includes memory 1004, which is coupled to theprocessor(s) 1002. The memory 1004 may be used for storing data,metadata, and programs for execution by the processor(s). The memory1004 may include one or more of volatile and non-volatile memories, suchas Random-Access Memory (“RAM”), Read-Only Memory (“ROM”), a solid-statedisk (“SSD”), Flash, Phase Change Memory (“PCM”), or other types of datastorage. The memory 1004 may be internal or distributed memory.

The computing device 1000 includes a storage device 1006 includingstorage for storing data or instructions. As an example, and not by wayof limitation, the storage device 1006 can include a non-transitorystorage medium described above. The storage device 1006 may include ahard disk drive (HDD), flash memory, a Universal Serial Bus (USB) driveor a combination these or other storage devices.

As shown, the computing device 1000 includes one or more I/O interfaces1008, which are provided to allow a user to provide input to (such asuser strokes), receive output from, and otherwise transfer data to andfrom the computing device 1000. These I/O interfaces 1008 may include amouse, keypad or a keyboard, a touch screen, camera, optical scanner,network interface, modem, other known I/O devices or a combination ofsuch I/O interfaces 1008. The touch screen may be activated with astylus or a finger.

The I/O interfaces 1008 may include one or more devices for presentingoutput to a user, including, but not limited to, a graphics engine, adisplay (e.g., a display screen), one or more output drivers (e.g.,display drivers), one or more audio speakers, and one or more audiodrivers. In certain embodiments, I/O interfaces 1008 are configured toprovide graphical data to a display for presentation to a user. Thegraphical data may be representative of one or more graphical userinterfaces and/or any other graphical content as may serve a particularimplementation.

The computing device 1000 can further include a communication interface1010. The communication interface 1010 can include hardware, software,or both. The communication interface 1010 provides one or moreinterfaces for communication (such as, for example, packet-basedcommunication) between the computing device and one or more othercomputing devices or one or more networks. As an example, and not by wayof limitation, communication interface 1010 may include a networkinterface controller (NIC) or network adapter for communicating with anEthernet or other wire-based network or a wireless NIC (WNIC) orwireless adapter for communicating with a wireless network, such as aWI-FI. The computing device 1000 can further include a bus 1012. The bus1012 can include hardware, software, or both that connects components ofcomputing device 1000 to each other.

In the foregoing specification, the invention has been described withreference to specific example embodiments thereof. Various embodimentsand aspects of the invention(s) are described with reference to detailsdiscussed herein, and the accompanying drawings illustrate the variousembodiments. The description above and drawings are illustrative of theinvention and are not to be construed as limiting the invention.Numerous specific details are described to provide a thoroughunderstanding of various embodiments of the present invention.

The present invention may be embodied in other specific forms withoutdeparting from its spirit or essential characteristics. The describedembodiments are to be considered in all respects only as illustrativeand not restrictive. For example, the methods described herein may beperformed with less or more steps/acts or the steps/acts may beperformed in differing orders. Additionally, the steps/acts describedherein may be repeated or performed in parallel to one another or inparallel to different instances of the same or similar steps/acts. Thescope of the invention is, therefore, indicated by the appended claimsrather than by the foregoing description. All changes that come withinthe meaning and range of equivalency of the claims are to be embracedwithin their scope.

What is claimed is:
 1. A non-transitory computer-readable medium storinginstructions thereon that, when executed by at least one processor,cause the at least one processor to perform operations comprising:identifying, from a digital behavior log corresponding to a user, a setof digital action sequences corresponding to a set of sessions for atask executed by the user, each digital action sequence comprising atask-identifying digital action, a first subset of digital actions thatchronologically precedes the task-identifying digital action, and asecond subset of digital actions that chronologically follows thetask-identifying digital action; generating, utilizing a machinelearning model, session weights indicating a predicted influence of theset of sessions for the task on a future session for the task; andproviding, for display on a graphical user interface, a visualindication of an action-selection bias of the user for the task based onthe session weights.
 2. The non-transitory computer-readable medium ofclaim 1, wherein providing the visual indication of the action-selectionbias comprises generating a graphical indication of an anchoring bias bywhich an earlier session for the task influences a digital actionsequence in the future session for the task more than a later sessionfor the task.
 3. The non-transitory computer-readable medium of claim 1,wherein providing the visual indication of the action-selection biascomprises generating a graphical indication of a recency bias by which alater session for the task influences a digital action sequence in thefuture session for the task more than an earlier session for the task.4. The non-transitory computer-readable medium of claim 1, furthercomprising instructions that, when executed by the at least oneprocessor, cause the at least one processor to perform operationscomprising: receiving, from a client device associated with the user,one or more digital actions corresponding to a session for the task;determining that the one or more digital actions are associated with theaction-selection bias of the user; and providing the visual indicationof the action-selection bias of the user based on determining that theone or more digital actions are associated with the action-selectionbias of the user.
 5. The non-transitory computer-readable medium ofclaim 1, wherein providing the visual indication of the action-selectionbias of the user comprises generating a graphical representation of thesession weights.
 6. The non-transitory computer-readable medium of claim1, further comprising instructions that, when executed by the at leastone processor, cause the at least one processor to perform operationscomprising: providing, for display on the graphical user interface, thevisual indication of the action-selection bias of the user for the taskwith a set of visual indications of action-selection biases of a set ofusers; receiving, via the graphical user interface, a user selection ofthe visual indication of the action-selection bias of the user; andproviding, for display on the graphical user interface, a graphicalrepresentation of frequencies of digital actions selected by the useracross the set of sessions.
 7. The non-transitory computer-readablemedium of claim 1, wherein identifying each digital action sequencecomprises: identifying, within the digital behavior log corresponding tothe user, the task-identifying digital action corresponding to the task;selecting, from within the digital behavior log, the first subset ofdigital actions by selecting digital actions that chronologicallyprecede the task-identifying digital action and are within a thresholdnumber of digital actions from the task-identifying digital action; andselecting, from within the digital behavior log, the second subset ofdigital actions by selecting digital actions that chronologically followthe task-identifying digital action and are within the threshold numberof digital actions from the task-identifying digital action.
 8. Thenon-transitory computer-readable medium of claim 1, wherein generatingthe session weights utilizing the machine learning model comprises:generating, utilizing the machine learning model, a predicted digitalaction sequence corresponding to the future session for the task; andgenerating the session weights based on a comparison of the predicteddigital action sequence with an observed digital action sequencecorresponding to the future session for the task.
 9. The non-transitorycomputer-readable medium of claim 8, wherein generating the sessionweights utilizing the machine learning model comprises: identifying,within the digital behavior log, a subsequent future session for thetask among a set of rolling windows of sessions that progressivelyfollow the set of sessions for the task; generating, utilizing themachine learning model, an additional predicted digital action sequencefor the task corresponding to the subsequent future session for thetask; and adjusting the session weights based on a comparison of theadditional predicted digital action sequence with an additional observeddigital action sequence corresponding to the subsequent future sessionfor the task.
 10. The non-transitory computer-readable medium of claim1, wherein generating the session weights indicating the predictedinfluence of the set of sessions for the task on the future session forthe task comprises generating, utilizing the machine learning model, thesession weights indicating a predicted influence of a frequency ofdigital actions within one or more sessions from the set of sessions onthe future session for the task.
 11. A system comprising: at least onememory device comprising a digital behavior log for one or more tasksexecuted by a user and an attention neural network; and at least oneserver device configured to cause the system to: identify, from thedigital behavior log, a set of digital action sequences corresponding toa set of sessions for a task from the one or more tasks, each digitalaction sequence comprising a task-identifying digital action, a firstsubset of digital actions that chronologically precedes thetask-identifying digital action, and a second subset of digital actionsthat chronologically follows the task-identifying digital action;generate, utilizing the attention neural network, a predicted digitalaction sequence corresponding to a subsequent session for the task;extract, from the attention neural network, attention weights indicatinga predicted influence of the set of sessions for the task on thesubsequent session for the task; and provide, for display on a graphicaluser interface, a graphical representation of the attention weightsdepicting an action-selection bias of the user for the task.
 12. Thesystem of claim 11, wherein the at least one server device is furtherconfigured to cause the system to provide the graphical representationof the attention weights by generating a graphical representation of ananchoring bias by which earlier sessions for the task are associatedwith greater attention weights than later sessions for the task.
 13. Thesystem of claim 11, wherein the at least one server device is furtherconfigured to cause the system to provide the graphical representationof the attention weights by generating a graphical representation of arecency bias by which later sessions for the task are associated withgreater attention weights than earlier sessions for the task.
 14. Thesystem of claim 11, wherein the at least one server device is furtherconfigured to cause the system to provide the graphical representationof the attention weights by generating a graphical representation ofchanges to the attention weights across a set of rolling windows ofsessions for the task that correspond to the user.
 15. The system ofclaim 11, wherein the at least one server device is further configuredto cause the system to: provide, for display on the graphical userinterface, the graphical representation of the attention weightsdepicting the action-selection bias of the user together with graphicalrepresentations of sets of attention weights corresponding to a set ofusers; receive, via the graphical user interface, a user selection ofthe graphical representation of the attention weights depicting theaction-selection bias of the user; and provide, for display on thegraphical user interface, a frequency heat map indicating frequencies ofdigital actions selected by the user across the set of sessions.
 16. Thesystem of claim 11, wherein the at least one server device is furtherconfigured to cause the system to generate the predicted digital actionsequence corresponding to the subsequent session for the task by:generating, utilizing an action-level encoder of the attention neuralnetwork, a set of action-level context vectors corresponding to the setof sessions; generating, utilizing a session-level encoder of theattention neural network, a session-level context vector based on theset of action-level context vectors; and generating, utilizing a decoderof the attention neural network, the predicted digital action sequencebased on the session-level context vector.
 17. The system of claim 16,wherein the at least one server device is further configured to causethe system to: identify a set of attention weights corresponding to anattention mechanism of the session-level encoder; adjust the set ofattention weights based on a comparison of the predicted digital actionsequence with an observed digital action sequence corresponding to thesubsequent session for the task; and extract the attention weights byextracting the adjusted set of attention weights from the attentionmechanism of the session-level encoder.
 18. The system of claim 11,wherein the at least one server device is further configured to causethe system to: generate, utilizing the attention neural network,baseline attention weights based on digital action sequencescorresponding to sessions for the task executed by a set of users; andgenerate, utilizing the attention neural network, the attention weightsbased on the baseline attention weights and the set of digital actionsequences corresponding to the user.
 19. A computer-implemented methodcomprising: identifying, from a digital behavior log corresponding to auser, a set of digital action sequences corresponding to sessions for atask executed by the user, each digital action sequence comprising atask-identifying digital action, a first subset of digital actions thatchronologically precedes the task-identifying digital action, and asecond subset of digital actions that chronologically follows thetask-identifying digital action; performing a step for determiningsession weights for the sessions utilizing a machine learning model; andgenerating a graphical user interface comprising a visual indication ofan action-selection bias corresponding to the user for the task based onthe session weights, the session weights indicating a predictedinfluence of the sessions for the task on a future session for the task.20. The computer-implemented method of claim 19, wherein identifying theset of digital action sequences corresponding to the sessions for thetask comprises: identifying, within the digital behavior logcorresponding to the user, a set of task-identifying digital actionsthat correspond to the task; and selecting, from within the digitalbehavior log, a set of digital actions from within a threshold number ofdigital actions of each task-identifying digital action.